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Article

Predictive Analysis of Adaptation to Drought of Farmers in the Central Zone of Colombia

by
Jorge Armando Hernández-López
1,*,
Diana Ximena Puerta-Cortés
2 and
Hernán J. Andrade
3
1
Civil Engineering Program, Engineering Faculty, Universidad de Ibagué, Carrera 22 Calle 67 B/Ambalá, Ibagué 730001, Colombia
2
Psychology Program, Universidad de Ibagué, Carrera 22 Calle 67 B/Ambalá, Ibagué 730001, Colombia
3
Research Group PROECUT, Department of Production and Plant Health, Faculty of Agronomic Engineering, Universidad del Tolima, Ibagué 730006, Colombia
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(16), 7210; https://doi.org/10.3390/su16167210
Submission received: 20 June 2024 / Revised: 5 August 2024 / Accepted: 8 August 2024 / Published: 22 August 2024
(This article belongs to the Topic Climate Change Impacts and Adaptation: Interdisciplinary Perspectives)
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Abstract

:
Drought constitutes one of the natural phenomena that causes the greatest socio-economic, and environmental losses in both the short and long term worldwide. Each year, these events are related to the presence of “El Niño—Southern Oscillation” (ENSO), which occurs throughout Colombia and has serious consequences in the agricultural and food sectors, as well as in most of the country’s population. Farmers have adopted a number of strategies to mitigate the negative impact of droughts on food production. Certainly, when implementing future strategies, such strategies will be less effective if farmers’ insights on ENSO are not considered. Consequently, this study was carried out to analyze the variables that predict adaptation to droughts in the dry zones of the department of Tolima. Three questionnaires were designed: socioeconomic vulnerability (SVT), risk perception (SRPT) and drought adaptation (SAT). A non-probability sample of 538 farmers was surveyed. Socio-economic vulnerability and drought perception were found to be predictive of drought adaptation in the study sample, and older people were found to be resilient to adaptation. The results of this research provide empirical evidence to analyze and formulate public policies about the impact of droughts on the most vulnerable populations.

1. Introduction

Global climate change affects agriculture and the lives of rural communities, threatening food sovereignty and rural people’s sustainability, especially in developing countries [1,2,3,4,5]. Colombia is located on the equatorial strip, so it is under the influence of the Intertropical Confluence Zone (ITCZ), a determining factor in the spatiotemporal distribution of precipitation, temperature, and other climatic variables [2,6,7]. Due to its location in the northwest of South America and the influence of the processes that occur in the Atlantic and Pacific Oceans, Colombia is a country susceptible to catastrophes and natural disasters [7,8]. Frequently, the warm phase of the intertropical convergence zone oscillation, the Southern Oscillation System (ENSO), causes droughts, disrupting normal climate patterns [8,9,10]. The ENSO phenomenon has a strong effect on precipitation, the flow of rivers, and soil moisture [10]. The warming phase (El Niño) is associated with an increase in average air temperature, a decrease in soil moisture and evapotranspiration, a decrease in precipitation, and a consequent decrease in the average flow of rivers in western regions, the center, and the north of the country [6]. The opposite pattern, the cold reverse phase of ENSO, known as La Niña, is characterized mainly by sharp and abundant rainfall, increased river flow and subsequent flooding, and intensification of trade winds in the eastern Pacific Ocean, causing relatively colder deep waters along the equatorial Pacific, remain at the surface [11]. The abnormally intense trade winds exert a greater drag effect on the ocean surface, increasing the difference in sea level between both ends of the equatorial Pacific [12].
Unlike other natural disasters, drought is one of the most complex and least understood climatic phenomena, because of its irregular distribution in time and space [13,14,15]. For this reason, it is difficult to provide a generic approach that includes all aspects and meets all expectations [2], which is why drought responds more to a climatic and environmental peculiarity, making it a phenomenon with a relative and elusive character [16].
Farmers suffer significant economic losses due to drought impacts over several years [16,17]. Crop production depends on financial, human, and social capitals that function as a whole system in which each capital influences and is influenced by other capitals, so the effects can be positive or negative [18,19]. To improve food livelihoods facing droughts, farming communities must develop context-specific adaptation measures [20,21]. Adaptation is defined as adjustments in ecological-socioeconomic systems in response to actual or expected climatic stimuli, effects, or impacts [22,23]. These adaptations can be autonomous, involuntary, planned, passive, reactive, or anticipatory and, to be successful, must address the sociopolitical, economic, and cultural interactions that determine access to livelihood resources [24,25]. Increasingly, adaptation studies recognize that people are not merely passive recipients, but also can overcome and adapt to change [26], albeit influenced by access to resources, local power dynamics, and other sociocultural, economic, and political dynamics [22].
Farming communities develop adaptation measures to minimize their vulnerability to droughts. Being vulnerable is defined as the degree and extent to which a specific hazard will affect the community and the environment [27,28,29,30]. There are three components to assess vulnerability: adaptive capacity, sensitivity, and exposure [31,32,33]. The ability of a structure to successfully adapt to climate extremes and variability is known as adaptive capacity [32,34,35]. Sensitivity refers to the responsiveness of the system to climatic influences and the degree to which climate change may affect its current form [27,36]. The nature, duration, and degree to which a system is exposed to considerable changes in climate are known as exposure [31].
Ongoing exposure to droughts means that communities that reside in agricultural areas have sufficient information to be able to perceive the risk. Drought risk perception is associated with education, resource acquisition, and awareness [37]. Understanding farmers’ perceptions can play an important role in ensuring that drought adaptation strategies are successfully implemented and can improve food production [19,20,38].
The perceptions of farmers provide information for assessing climate change impacts as well as drought adaptation strategies [39,40]. Data on farmers’ perceptions of drought are influenced by socioeconomic (gender, education level, household size, age, land tenure, off-farm income, livestock ownership), biophysical (agroecology, farm size and distance to market, rainfall and temperature), and institutional factors (extension services, access to credit, climate information, and humanitarian aid) [34,41,42,43].
Droughts have affected social welfare and food security throughout human history [10,44,45]. It is one of the most devastating climatic threats to agriculture, because it causes the stoppage of food production. It also affects the growth of pastures, alters markets, and in extreme cases, results in the widespread death of animals [39,46,47,48].
From the socioeconomic perspective, one key factor that could drive or constrain the perception and adaptation to climate variability is gender [41]. Age is also an important socioeconomic factor in the perception and uptake of drought adaptation strategies [41]. Older farmers are prone to risk and are better at recognizing changes and using adaptation practices than younger smallholder farmers [41,49]. Older farmers also have more experience in farming and are more exposed to past climate shocks than younger farmers [41]. If most family members are of working age, the household will have enough labor and will be using better coping strategies [41,50].
Understanding farmers’ perceptions can play an important role so that drought adaptation strategies are successfully implemented and can improve food production [51,52]. Despite the studies that have been carried out in Colombia so far, there is little research related to adaptation, socioeconomic vulnerability, and risk perception of farmers to droughts [53]. Therefore, there are no data to carry out effective interventions to minimize the impact of droughts in these communities. This research aims to analyze the variables that predict adaptation to droughts in the drylands of the department of Tolima in Colombia.
This research aims to analyze the variables that predict adaptation to droughts. The study is organized as follows: Section 2 includes a brief description of the study area, participants, questionnaires administered, and data analysis; the main results are presented in Section 3; Section 4 presents the discussion; and Section 5 shows the main conclusions. Finally, an annex of the questionnaires applied is included.

2. Materials and Methods

2.1. Spatial Coverage of the Survey

This research was developed in the dry area of the department of Tolima, located in the Alto Magdalena Valley, in central Colombia. The study area is located between 194 and 1000 m of altitude and has an area of 10,082.9 km2. (Figure 1). The climatic conditions of these landscape units are characterized by an average annual temperature greater than 27 °C, average annual rainfall less than 1500 mm, and elevations between 300 and 400 m.
Historically, the territory’s climate has been affected by ENSO, such as it has seen in the last 25 years (1985, 1988, 1991–1992, 1997–1998, 2001, 2009, 2015, and 2016) [54]. ENSO has negatively impacted the generation of electrical energy, shortages of agricultural products, an increase in diseases, scarcity of drinking water, pests, and a reduction in agricultural productivity [55,56].
The Magdalena River crosses the department from south to north, affecting the weather in the area. The bioclimatic provinces in the study area are warm arid (CA), warm semi-arid (Csa), and warm semi-humid (Csh). The rain regime is bimodal, which is characterized by two quarterly rainy seasons between March and May, and from October to December, alternating with two seasons of low precipitation from January to February and from June to September [57]. In this ecosystem, there is great variation in physiographic units, presenting landscapes of alluvial valleys, hills, foothills, and mountains [56].
The land of the study area is mainly covered by natural pastures, managed pastures dedicated to extensive livestock grazing, cultivation of rice, cotton, corn, sorghum, small fruit crops, and vegetables, which are the main economic activities [57]. In the 43 municipalities of the study area, land use is generally distributed by mechanized and irrigated lands, mostly covered by natural pastures [56].
The natural cover has been almost completely destroyed, and the landscape is currently characterized by small groups of secondary forest and trees in living fences and pastures. Likewise, the productivity of livestock systems is limited mainly by the drastic decrease in forage, especially as a consequence of intense periods of drought, further accentuated by the high degree of deforestation. This situation causes low rates of productivity and profitability in both the agricultural and livestock systems of the area [57].

2.2. Survey Participants

The sample of the study was selected from 28 municipalities in the department of Tolima, Colombia. A total of 538 farmers participated; 24.7% of them were women and 75.2% were men. The respondents ranged in age from 18 to 85 years old. In the age group distribution, they were classified as: young people between 18 and 25 years old (8.8%); adults between 26 and 55 years old (57.5%) and older adults between 56 and 85 years old (33.7%). The predominant educational level in the questionnaires was primary school with 40.9%, followed by secondary school with 39.6%.
Data collection was carried out through a form applied by previously trained research assistants, who traveled to the dry zones of the study area. The respondents, whose participation was voluntary, were located in the population centers, at neighborhood meetings and on farms. Before the application of the questionnaire, the interviewers explained the objectives of the study to those interested, to them then it was shared the form containing the informed consent (voluntary participation, confidentiality, freedom to withdraw from the study and anonymity of their answers) and the questionnaires [58,59]. The application of the questionnaires was carried out from February to June 2022. The questionnaire was peer-reviewed to ensure its comprehensibility and acceptability by farmers [60,61].

2.3. Sample Survey

Different steps were taken to develop the SPRT and SAT questionnaires based on the methodology of McCoach [62]. This approach consists of a review of the literature to define the constructs, then the variables were operationalized and the items were drafted. In particular, the SPRT questionnaire was based on the theoretical aspects of Mooney [63], who identified and defined the three dimensions of risk perception in the face of climate change: perceived probability, perceived severity, and perceived consequences. The questionnaires on Drought Adaptation (SAT) and Socioeconomic Vulnerability (SVT) did not contain dimensions, therefore, they are categorized as unidimensional, and they only evaluate one construct.
After the questionnaires were designed, two experts evaluated the items of the scale, making linguistic adjustments, and then the scale was applied to 35 people, in order to obtain an initial evaluation. A four-point Likert scale was used to check whether the participants understood the item descriptions. The results showed that all respondents fully understood the item descriptions. No modifications were made to the questionnaire or the scale, and the questionnaires were then applied to the study sample (see Appendix A).
Four questionnaires were applied: The first questionnaire, “Socio-demographic data and lifestyle in drought”, consisted of 28 questions that inquired about the place of residence, name of the farm, name of the village, number of children, number of household members, main source of family income and income. Also, a total of 16 questions related to behaviors associated with the farmers’ lifestyle during drought were formulated. These questions focused on four areas: land tenure, infrastructure and tools, access to water for irrigation and drinking water. The variables such as housing, time in housing and type of fuel for cooking (cooking with) are part of the characterization of the study sample in the section on sociodemographic variables which describe multidimensional poverty. For this reason, these elements may be compatible with other samples due to that most drought regions include farmers in poor areas.
The second questionnaire, “Socio-economic Vulnerability (SVT)”, which was designed to evaluate social and economic vulnerability to droughts, consisted of 12 questions with a four-point Likert-type response, where one equals “Strongly disagree” and four equals “Strongly agree”. The reliability analysis through Cronbach’s alpha coefficient was 0.74 and showed a percentile classification in low vulnerability (27.7%), medium vulnerability (45.9%) and high vulnerability (26.4%).
Questionnaire number 3, “Drought risk perception (SPRT)”, was designed with the objective of evaluating the farmers’ perceptions of droughts. The SRPT consists of two climate perception questions and three dimensions: perceived probability with five questions, perceived severity with eight questions and perceived consequence with 14 questions. The Likert-type response scale is a four-point scale, where one equals “Strongly disagree” and four equals “Strongly agree”. The reliability analysis of the SRPT using Cronbach’s alpha = 0.88; for the perceived likelihood dimension 0.73; perceived severity 0.79 and perceived consequence 0.71.
The last questionnaire, “Drought Adaptation Test (SAT)”, was designed with the objective of evaluating the adaptation of farmers to droughts. It consists of 12 questions with a four-point Likert-type response scale, where one equals “Strongly disagree” and four equals “Strongly agree”. The reliability of the questionnaire through Cronbach’s alpha was 0.76.

2.4. Survey Data Analysis

The data analysis was carried out according to the type of variables, the objectives of the study, and the scope of the research. This analysis started with bivariate analysis, then correlational, the reliability of the instruments was carried out, which corresponds to the measurement of the degree of stability of the results of the questionnaires when a measurement is repeated under identical conditions. Finally, a linear regression analysis to detect predictor variables was carried out.
The Statistical Package for the Social Sciences 25.0 (IBM SPSS), Rstudio, and Microsoft Excel v-2010 were used for data analysis [64,65,66]. We performed a bivariate analysis using the X2 test to detect and describe the relationship between sociodemographic variables, lifestyle-associated behaviors, SVT, SRPT, and SAT. The linear correlation between each of the dimensions (SRPT, SVT, and SAT) was estimated using the Pearson coefficient. The reliability of the questionnaires was assessed using Cronbach’s Alpha internal consistency index, which was calculated using the item variance method, and a linear regression analysis was performed to detect predictor variables [54,59,67,68].

3. Results

3.1. Characterization of the Sample

A total of 73.8% of the participants in the study have lived in the study area for more than 5 years; 62.3% have their own home; 40.7% of the family group is made up of 3 to 4 people; the main source of income is agriculture (82.7%); and 41.1% earn less than one million pesos (Table 1).
Most of the farmers (58.6%) own the land or plot of land, and 55.8% have their own agricultural machinery. About 55.4% of farmers have one to two workers, 84.5% have a maximum of 10 hectares of land, and 54.8% have access to water for irrigation purposes, while 56.3% do not have access to drinking water (Figure 2).

3.2. Correlational Analysis between Age and Questionnaire Scores

The correlational analysis showed: (a) a negative and significant (p < 0.05) relationship between age and drought adaptation, and socioeconomic vulnerability index scores; (b) a positive and significant (p < 0.05) relationship between SAT, SVT, and SRPT index scores; and (c) and finally, a positive and significant (p < 0.05) relationship between SVT and SRPT (Table 2).
A linear regression was carried out to identify the predictor variables of farmers in the face of drought. The dependent variable was SAT scores. The covariates were age, SVT and SRPT scores, socioeconomic vulnerability and perception were found to predict adaptation to drought in the study sample. As shown in Table 3, the model achieved a correct classification of 89.8% of the participants. The greatest resistance to change was evidenced by older people (95% CI = −0.047–0.020); with the greater vulnerability, more adaptation strategies are developed (95% CI = 0.405–0.576); people who have a perception of droughts assume strategies to adapt easily (95% CI = 0.026–0.106). Vulnerability and perception are predictors of adaptation to drought.

4. Discussion

Questionnaires are a useful tool to provide an overview of droughts and provide feedback on participants’ current perceptions [69]. Responses are based on subjective experiences and could be influenced temporarily or permanently by external factors, e.g., media coverage of current droughts, heat waves, and personal interest in the topic [70,71]. The SPRT risk perception questionnaire contained three dimensions: perceived probability, perceived severity, and perceived consequences. The SAT and SVT questionnaires were unidimensional. Table 4 describes the results of the questionnaires and also each dimension of the SPRT.
Knowledge and adaptation processes that are useful for individuals or communities to effectively adjust to modified environments over time are largely derived from empirical and analogical analyses [70,72]. The results of this study identified that agricultural land size, geographic variations, and the number of adaptation activities implemented are key factors in recognizing variables that predict adaptation to drought in and around dry areas.
The study survey captured a representation of 73% male and 25% female farmers. Gender identity within agricultural communities meant potentially male participation in the dry areas of the department of Tolima. Most of the farmers surveyed, 309 respondents, were between 26 and 55 years old. This suggests that middle-aged and slightly older people were prominently engaged in agricultural activities. Furthermore, a substantial proportion of the farmers were within the age group of 56 to 85 years, possibly indicating a persistence of older people in the agricultural sector, such as those proposed by other authors [73,74,75].
The effectiveness of adaptations depends on socioeconomic factors and human system capacity that influence the selection of adaptation measures [76]. In our study, land tenure, access to water, level of schooling, size of agricultural land for cultivation, crop variations, and amount of implemented adaptation activities were identified as key factors determining the choice of adaptation measures. Farmers with access to water and larger farmland sizes are more likely to choose and adopt drought adaptation [77]. These factors also play an important role in determining an individual’s economic status, whether they are wealthy or living in poverty [78,79].
Farmers’ perceptions of drought impacts, and their adaptations, play a crucial role in reducing the effects of climate change on crop production [52,80,81]. Maintaining agricultural production is essential for farmers, but drought is severely influencing this [82]. The results show that vulnerability and perception are predictive of drought. Therefore, farmers’ perception was identified as the dependent variable, which is widely used in similar studies [21]. According to the theoretical background of previous studies [52,60], people who perceive drought as a high-risk phenomenon seek adaptation strategies.
Scarcity of water, an increase in crop diseases, a reduction in crop yields, increased pest incidence, and a change in planting times are among the worst impacts of climate-related risks [83]. Table 5 compares the variables used in this study with research from other continents and Latin America. The study results provide a good overview of the challenges faced by farmers, focusing on the effects of droughts, and their preferred strategies to mitigate these effects. The older you are, the less adaptation you have, so there is greater resistance to change. People who perceive greater vulnerability are those who adapt faster because they perceive greater adaptation strategies.

5. Conclusions

The SAT and SVT questionnaires were designed as a unidimensional tool to assess adaptation to drought. The SPRT is a valid and reliable questionnaire to evaluate the impact that drought has on farmers in Tolima. Therefore, the data collected help to identify the strategies to counteract the impact that this type of event has on the person and his community.
Older adults are the ones least able to adapt to droughts, so it is recommended that a detection and intervention program be developed to identify how this situation is affecting their health because of the stress caused by the phenomenon. It is also important to generate some community guidelines or public policies, so that the elderly can face droughts and improve their quality of life. However, the people who scored higher in their perception of droughts developed coping strategies, such as adaptation measures, since they cannot leave their territories due to socioeconomic reasons or security. According to our study, drought affects the agricultural economy, which generates food insecurity. The vulnerability of farmers living in these areas is associated with the lack of infrastructure, changes in rainfall and temperature patterns, and the lack of support from governmental and non-governmental institutions to solve the shortage of drinking water, and limited access to social, financial and human capital. Because of these conditions, farmers are even more vulnerable to the impacts of drought.
It is important to recognize that local communities are the main actors in addressing drought problems through a combination of traditional and contemporary practices developed through sustained interactions with their local environment. These results will enable policy makers, scientists, and program designers to realize adaptation strategies to ensure successful policy implementation. Likewise, having knowledge about droughts encourages farmers to adopt adaptation measures. Farmers will naturally play the most important role in adopting and implementing available adaptation methods, and the questionnaire results show the willingness of farmers to adopt adaptation measures.
The data from the questionnaires designed in this study allow developing countries, such as Colombia, to make decisions regarding risk management strategies in areas of significant agricultural potential, but with complex social needs and drought problems, in order to minimize the impact of droughts through the design of early warnings and effective interventions.
One of the limitations of the study is the use of a self-administered questionnaire and the participants’ responses are based on subjective experiences and could be temporarily or permanently influenced by external factors, such as media coverage of current droughts, heat waves and personal interest in the topic. However, self-administered questionnaires have several advantages thanks to the flexible modality that allows them to be applied to a greater number of people, there is no pressure to answer the questions in a period of time, and they have a low investment. For future research, it is proposed to apply the instruments in other geographical areas to identify their internal consistency and their ability to generalize the data.

Author Contributions

Conceptualization, J.A.H.-L., D.X.P.-C. and H.J.A.; methodology, J.A.H.-L. and D.X.P.-C.; validation, J.A.H.-L., D.X.P.-C. and H.J.A.; formal analysis, J.A.H.-L., D.X.P.-C. and H.J.A.; investigation, J.A.H.-L. and D.X.P.-C.; data curation, J.A.H.-L., D.X.P.-C. and H.J.A.; writing—original draft preparation, J.A.H.-L., D.X.P.-C. and H.J.A.; writing—review and editing, J.A.H.-L., D.X.P.-C. and H.J.A.; visualization, J.A.H.-L., D.X.P.-C. and H.J.A.; supervision, J.A.H.-L., D.X.P.-C. and H.J.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the University of Ibagué [research project 19-500-INT, 2018].

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A. Questionnaire to Evaluate Water Availability in Drought Scenarios

Before starting, please read the following:
The University of Ibagué is leading the project “Evaluation of the effect of water availability on agricultural production in the department of Tolima in drought scenarios.” This questionnaire is part of the research project, and its objective is to evaluate the adaptation measures that agricultural workers use to protect their crops from drought scenarios (El Niño phenomenon), and how these scenarios affect agricultural, environmental, and social work, and the economy of the people.
Participation is completely voluntary and anonymity will be ensured, it does not present any risk to well-being and as a participant you are free to withdraw.
It is important that you answer with complete sincerity for the study to be successful, remember that there are no right or wrong answers.
Based on the above, you wish to participate in the study: Yes ___ No ___
General data
Age (Numbers): ______ Sex: F__ M___
Marital status: Single ____ Married ____ Widowed ___ Common law ____ Divorced ____
Level of education: Primary ___ Secondary ___ Baccalaureate ___ University ____ Technical studies ___ Postgraduate studies (Specialization, master’s degrees and/or doctorate) ___
Place of birth: ________________________
Sociodemographic data questionnaire
1. Municipality where you live: ________________________
2. Name of the property:___________________________
3. Name of the Vereda: _____________________
4. Number of household members: 1 member ____ 1 to 2 members ___ 3 to 4 members ___ More than 5 members ___
5. Do you have children: Yes ___ No ___
6. If you have children, how many are they? 1 child ___ 2 to 3 children ___ 3 to 4 children ___ More than 5 children ___ I do not have children ___
7. How many people do you have in charge?: 1 person ___ 2 people ___ More than 3 people ___ I live alone ___ I don’t have people in charge ___
8. Monthly income: Less than $1,000,000 ___ $1,000,000 ___ Between $1,000,000 to 2,000,000 ___ Between $2,000,000 to 3,000,000 ___ Between $4,000,000 to $5,000,000 ___ More than $5,000,000 ___
9. Housing: Own ___ Rented ___ Family _______
10. Time of residence in the home: Less than a year __ Between 1 to 2 years __ 3 to 4 years ___ More than 5 years ___
11. Cooking with: Natural gas ___ Firewood ___ Gas pipette ___
12. What is your family’s main source of income? Livestock _ Agriculture _ Fish farming _ Commerce _ Other: _____
Land tenure
13. The plot or property where you work is: Owner ___ Leased ___ Usufructuary ___ Other ___
14. What infrastructure do you have on your farm or plot? Greenhouse ___ Warehouse ___ Shed ___ Outdoors ___ Other: __________________
Infrastructure and tools
15. For agriculture, what tools do you use: Agricultural machinery ___ Work animals ___ Hand tools (pick, shovel, hoe, back pump, wheelbarrow, etc.) ___ Other: ______________________
16. Uses machinery: Own ___ Rented ___
17. Number of workers in charge: 1 to 2 workers ___ 3 to 4 workers ___ 4 to 5 workers ___ More than 5 workers ___
18. Time spent farming (in years): Less than 1 year _ 1 year __ 1 to 2 years __ 2 to 3 years __ 3 to 4 years __ More than 5 years ___
19. What do you grow at the moment? Vegetables __ Rice__ Wheat__ Beans__ Corn__ Other:_______________________
20. Size of the crop (hectares, bushels or square meters). Write with unit: _______________________
21. What other crops do you have during the year?: Cereals ___ Fruit trees ___ Legumes ___ Other: _____________
22. You have animals: Yes ___ No ___
23. Type of animals: I do not have animals___ Cows___ Goats___ Sheep___ Fattening steers___ Pack animals (Horses, mules, donkeys)___ Pigs___ Dogs___ Fish___ Ducks- Geese___ Chickens___ Other__________________
Water access for irrigation
24. Do you have access to water for irrigation: Yes ___ No ___
25. What are the sources of water supply for irrigation? Indicate the option with which you most identify according to the source of supply, with respect to scale (every week, from time to time, I have very little or no supply source):
Every WeekFrom Time to TimeNever
Channel
Well
Reservoir
Broken
River
Rainwater accumulation
Access to drinking water
26. Do you have access to drinking water: Yes ___ No ___
27. Since when you do not have drinking water: Less than 1 month ___ 1 to 2 months ___ 3 to 6 months ___ 1 year ___ More than 1 year ___ Never ____ Not applicable___
28. The source of water consumed for human use is obtained from: River ___ Rain ___ Irrigation district ___ Community or village aqueduct ___ Municipal aqueduct ___ Birth _____
Perception of drought
To answer questions 29 and 30, take into account the following concepts:
The term precipitation is used in meteorology to refer to all the phenomena of water falling from the sky in any form: rain, hail, snow, etc.
Droughts are prolonged periods of dry weather caused by a lack of rain, resulting in water shortages. Periods of drought can cause water shortages and public health problems.
29. Perception of drought (In recent years):
IncreasedDiminishedThere Are no ChangesDoes not Know
The rainfall has:
The drought has
30. Number of precipitations per year: Below you will find the months of the year in the rows and the types of precipitation in the columns. According to the month, indicate the type of precipitation (Rain, dry or rain-Dry) for each month according to your experience.
RainDryRain-Dry
January
February
March
April
May
June
July
August
September
October
November
December
Socioeconomic vulnerability to droughts:
Below you will find a series of questions related to the experiences you have had with the drought. Respond according to your experience and indicate the answer that best identifies you based on the following scale:
1 = Strongly disagree
2 = Disagree
3 = Agree
4 = Totally agree.
Items1234
31. I have water for field activities
32. I have drinking water for human consumption
33. I have drinking water for household chores
34. I have a rainwater storage system
35. I eat less than three meals a day.
36. I have a community that supports me economically, socially and/or emotionally
37. The access roads to my property are paved
38. There are conflicts over public order in my neighborhood
39. The floors of my home are made of dirt
40. The roof of my home is made of cardboard sheets or other recycling material
41. My home guarantees physical security; i.e., protection against cold, humidity, heat, rain, wind or other health risks and structural hazards
42. In my house, more than three adults sleep in a room.
Drought risk perception
Below you will find a series of statements related to what you believe or think about the drought, responding according to your experience with the drought. Indicate the answer according to the option that best identifies you.
Perceived probability:
In this section, you will find a series of questions related to the changes in your life that you have observed due to the drought. Respond according to your experience, indicating the option that best identifies you based on the following scale:
1 = Strongly disagree
2 = Disagree
3 = Agree
4 = Totally agree.
Items1234
43. Summer is a threat that affects the water sources of my plot or farm
44. I think the drought will come
45. I am worried about the consequences of the next drought
46. My family is worried that a drought is coming
47. The next drought will have worse consequences than the previous one
The perceived severity
In this section you will find a series of questions related to the negative impact that drought has on your life. According to your experience with drought, indicate the answer that best identifies you based on the following scale:
1 = Strongly disagree
2 = Disagree
3 = Agree
4 = Totally agree.
Items1234
48. Drought is the most serious thing that can happen to us
49. The drought greatly affects the health of my family, that of my workers and myself
50. Drought greatly affects the health and well-being of my animals
51. Drought damages the quality of my crops
52. The drought deteriorates my home
53. My crop is affected by pests and/or diseases caused by droughts.
54. The drought affects my harvest
55. Drought seriously reduces water in my farm or plot
Perceived consequences of droughts:
In this section, you will find a series of questions related to what you perceive causes the drought. Indicate the answer according to the option that best identifies you based on the following scale:
1 = Strongly disagree
2 = Disagree
3 = Agree
4 = Totally agree.
Items1234
56. Due to lack of water my family has health problems
57. During a drought, water sources (rivers, streams, wells, streams, among others) are reduced.
58. Water scarcity limits agricultural production
59. In times of drought my crop loses life (withers or dies)
60. A drought affects my agricultural work
61. A drought can cause great economic losses
62. I believe that in the event of a water shortage, children will have to miss school
63. Drought generates significant losses in crop production
64. Due to the drought I sell my harvest at a lower price
65. Due to the drought I sell my property
66. The scarcity of water affects the sanitary systems of my farm
67. Due to the drought, I sold part of my assets and/or took out a mortgage on my home to obtain resources for my livelihood.
68. I took out a bank loan due to the effects of the drought
69. Due to the drought I have thought about changing my place of residence.
Adaptation to droughts:
Below you will find a series of questions related to how you adapt or adjust to the drought, so please respond according to your experience, indicating the option that best identifies you based on the following scale:
1 = Strongly disagree
2 = Disagree
3 = Agree
4 = Totally agree.
Items1234
70. In the summer time he used seed that withstands the weather conditions
71. To reduce the effect of drought on my crops, I have used pesticides, insecticides, and other inputs to control pests or diseases.
72. Droughts have brought me new opportunities
73. Due to the effects of the droughts, I have thought about changing my economic activity
74. Due to the effects of the droughts I have changed jobs
75. I have looked for new water storage alternatives on my farm or property.
76. Despite the droughts, agriculture has generated stability for me
77. The drought has allowed me to improve my agricultural techniques
78. Due to the droughts I have had to carry out other extra activity(s) to increase income
79. Due to the droughts I have modified the irrigation system for my crop
80. Due to the droughts I began to use agrochemicals to improve production in my crop (insecticides, fungicides, pesticides, etc.)
81. I have learned to use less labor to harvest the harvest in case of drought in my crop.

References

  1. Acibuca, V.; Kaya, A.; Kaya, T. Interregional comparative analysis of farmers’ perceptions and expectations of climate change. Ital. J. Agron. 2022, 17, 1–8. [Google Scholar] [CrossRef]
  2. Ávila Díaz, Á.J.; Carvajal Escobar, Y.; Gutiérrez Serna, S.E. Análisis de la influencia de El Niño y La Niña en la oferta hídrica mensual de la cuenca del río Cali. Rev. Tecnura 2015, 18, 120–133. [Google Scholar] [CrossRef]
  3. Shah, A.A.; Khan, N.A.; Gong, Z.; Ahmad, I.; Naqvi, S.A.A.; Ullah, W.; Karmaoui, A. Farmers’ perspective towards climate change vulnerability, risk perceptions, and adaptation measures in Khyber Pakhtunkhwa, Pakistan. Int. J. Environ. Sci. Technol. 2022, 20, 1421–1438. [Google Scholar] [CrossRef]
  4. Streimikiene, D.; Dahal, K.R.; Dahal, P.; Adhikari, R.K.; Naukkarinen, V.; Panday, D.; Bista, N.; Helenius, J.; Marambe, B. Climate Change Impacts and Adaptation in a Hill Farming System of the Himalayan Region: Climatic Trends, Farmers&rsquo; Perceptions and Practices. Climate 2022, 11, 11. [Google Scholar] [CrossRef]
  5. Yadav, S.S.; Hegde, V.S.; Habibi, A.B.; Dia, M.; Verma, S. Climate Change, Agriculture and Food Security. In Food Security and Climate Change; Yadav, S.S., Redden, R.J., Hatfield, J.L., Ebert, A.W., Hunter, D., Eds.; John Wiley & Sons: New York, NY, USA, 2019; pp. 1–24. [Google Scholar] [CrossRef]
  6. Ojeda-Flechas, C.D.; Burbano-Rodríguez, J.A.; Carvajal-Escobar, Y.; Hernández-Torres, F.L. Characterization of comprehensive drought events associated with the enso warm phase through satellite images in the valle del cauca, colombia [Caracterización de eventos de sequía integral, asociados a la fase cálida de enos, mediante imágenes satelitale. DYNA 2020, 87, 204–214. [Google Scholar] [CrossRef]
  7. Valencia, J.M.; García, C.E.; Montero, D. Anomalías de vegetación asociadas con el fenómeno del ENOS en el valle geográfico del río Cauca, Colombia. Rev. Teledetec. 2017, 50, 89–100. [Google Scholar] [CrossRef]
  8. Poveda, G.; Álvarez, D.M.; Rueda, Ó.A. Hydro-climatic variability over the Andes of Colombia associated with ENSO: A review of climatic processes and their impact on one of the Earth’s most important biodiversity hotspots. Clim. Dyn. 2011, 36, 2233–2249. [Google Scholar] [CrossRef]
  9. Bedoya-Soto, J.M.; Poveda, G.; Trenberth, K.E.; Vélez-Upegui, J.J. Interannual hydroclimatic variability and the 2009–2011 extreme ENSO phases in Colombia: From Andean glaciers to Caribbean lowlands. Theor. Appl. Climatol. 2019, 135, 1531–1544. [Google Scholar] [CrossRef]
  10. Loaiza Cerón, W.; Carvajal Escobar, Y.; Baquero Montoya, O.L. Índice estandarizado de precipitación (SPI) para la caracterización de sequías meteorológicas en la cuenca del río Dagua-Colombia. Estud. Geogr. 2016, 76, 557–578. [Google Scholar] [CrossRef]
  11. Ballesteros, J.; Isaza, C. Adaptation Measures to Climate Change as Perceived by Smallholder Farmers in the Andes. J. Ethnobiol. 2021, 41, 428–446. [Google Scholar] [CrossRef]
  12. Henao, F.; Viteri, J.P.; Rodríguez, Y.; Gómez, J.; Dyner, I. Annual and interannual complementarities of renewable energy sources in Colombia. Renew. Sustain. Energy Rev. 2020, 134, 110318. [Google Scholar] [CrossRef]
  13. Chen, Y.; Penton, D.; Karim, F.; Aryal, S.; Wahid, S.; Taylor, P.; Cuddy, S.M. Characterisation of meteorological drought at sub-catchment scale in Afghanistan using station-observed climate data. PLoS ONE 2023, 18, e0280522. [Google Scholar] [CrossRef]
  14. Dahir, A.; Omar, M.; Abukar, Y. Internet of things based agricultural drought detection system: Case study Southern Somalia. Bull. Electr. Eng. Inform. 2023, 12, 69–74. [Google Scholar] [CrossRef]
  15. Espinosa, L.A.; Portela, M.M.; Matos, J.P.; Gharbia, S. Climate Change Trends in a European Coastal Metropolitan Area: Rainfall, Temperature, and Extreme Events (1864–2021). Atmosphere 2022, 13, 1995. [Google Scholar] [CrossRef]
  16. Velasco, I.; Ochoa, L.; Gutiérrez, C. Sequía, un problema de perspectiva y gestión. Región Y Soc. 2017, 17, 35–71. [Google Scholar] [CrossRef]
  17. Mpandeli, S.; Nesamvuni, E.; Maponya, P. Adapting to the Impacts of Drought by Smallholder Farmers in Sekhukhune District in Limpopo Province, South Africa. J. Agric. Sci. 2015, 7, 115–124. [Google Scholar] [CrossRef]
  18. Mavhura, E.; Manatsa, D.; Matiashe, M. Adapting smallholder farming to climate change and variability: Household strategies and challenges in Chipinge district, Zimbabwe. Clim. Chang. 2017, 3, 903–913. [Google Scholar]
  19. Mavhura, E.; Manyangadze, T.; Aryal, K.R. Perceived impacts of climate variability and change: An exploration of farmers’ adaptation strategies in Zimbabwe’s intensive farming region. GeoJournal 2022, 87, 3669–3684. [Google Scholar] [CrossRef]
  20. Ogundeji, A.A.; Okolie, C.C. Perception and Adaptation Strategies of Smallholder Farmers to Drought Risk: A Scientometric Analysis. Agriculture 2022, 12, 1129. [Google Scholar] [CrossRef]
  21. Van-Tilburg, A.J.; Hudson, P.F. Extreme weather events and farmer adaptation in Zeeland, the Netherlands: A European climate change case study from the Rhine delta. Sci. Total Environ. 2022, 844, 157212. [Google Scholar] [CrossRef] [PubMed]
  22. Griffin, C.; Wreford, A.; Cradock-Henry, N.A. ‘As a farmer you’ve just got to learn to cope’: Understanding dairy farmers’ perceptions of climate change and adaptation decisions in the lower South Island of Aotearoa-New Zealand. J. Rural Stud. 2023, 98, 147–158. [Google Scholar] [CrossRef]
  23. Smith, W.; Davies-Colley, C.; Mackay, A.; Bankoff, G. Social impact of the 2004 Manawatu floods and the “hollowing out” of rural New Zealand. Disasters 2011, 35, 540–553. [Google Scholar] [CrossRef] [PubMed]
  24. Eriksen, S.; Aldunce, P.; Bahinipati, C.S.; Martins, R.D.A.; Molefe, J.I.; Nhemachena, C.; O’Brien, K.; Olorunfemi, F.; Park, J.; Sygna, L.; et al. When not every response to climate change is a good one: Identifying principles for sustainable adaptation. Clim. Dev. 2011, 3, 7–20. [Google Scholar] [CrossRef]
  25. Rahman, H.M.; Albizua, A.; Soubry, B.; Tourangeau, W. A framework for using autonomous adaptation as a leverage point in sustainable climate adaptation. Clim. Risk Manag. 2021, 34, 100376. [Google Scholar] [CrossRef]
  26. Tanner, T.; Lewis, D.; Wrathall, D.; Bronen, R.; Cradock-Henry, N.; Huq, S.; Lawless, C.; Nawrotzki, R.; Prasad, V.; Rahman, M.A.; et al. Livelihood resilience in the face of climate change. Nat. Clim. Chang. 2015, 5, 23–26. [Google Scholar] [CrossRef]
  27. Derbile, E.K.; Chirawurah, D.; Naab, F.X. Vulnerability of smallholder agriculture to environmental change in North-Western Ghana and implications for development planning. Clim. Dev. 2022, 14, 39–51. [Google Scholar] [CrossRef]
  28. Fatorić, S. Vulnerability and Adaptation to Climate Change in the Mediterranean Region. Doctoral Thesis, Universidad Autónoma de Barcelona, Barcelona, España, 2014. [Google Scholar]
  29. Keshavarz, M.; Maleksaeidi, H.; Karami, E. Livelihood vulnerability to drought: A case of rural Iran. Int. J. Disaster Risk Reduct. 2017, 21, 223–230. [Google Scholar] [CrossRef]
  30. Sultana, R.; Irfanullah, H.M.; Selim, S.A.; Budrudzaman, M. Vulnerability and ecosystem-based adaptation in the farming communities of droughtprone Northwest Bangladesh. Environ. Chall. 2023, 11, 100707. [Google Scholar] [CrossRef]
  31. Eze, J.N.; Onokala, P.C. Pattern of household vulnerability to desertification in Yobe state, Nigeria. GeoJournal 2022, 87, 2699–2717. [Google Scholar] [CrossRef]
  32. Okolie, C.C.; Danso-Abbeam, G.; Ogundeji, A.A. Livelihood vulnerability to the changing climate: The experiences of smallholder farming households in the Free State Province, South Africa. Clim. Serv. 2023, 30, 100371. [Google Scholar] [CrossRef]
  33. Virnodkar, S.S.; Pachghare, V.K.; Patil, V.C.; Jha, S.K. Remote sensing and machine learning for crop water stress determination in various crops: A critical review. Precis. Agric. 2020, 21, 1121–1155. [Google Scholar] [CrossRef]
  34. Ali, S.; Ying, L.; Nazir, A.; Ishaq, M.; Shah, T.; Ye, X.; Tariq, A. Rural farmers perception and coping strategies towards climate change and their determinants: Evidence from Khyber Pakhtunkhwa province, Pakistan. J. Clean. Prod. 2021, 291, 125250. [Google Scholar] [CrossRef]
  35. Khan, I.; Lei, H.; Shah, I.A.; Ali, I.; Khan, I.; Muhammad, I.; Huo, X.; Javed, T. Farm households’ risk perception, attitude and adaptation strategies in dealing with climate change: Promise and perils from rural Pakistan. Land Use Policy 2020, 91, 104395. [Google Scholar] [CrossRef]
  36. Bahta, Y.T.; Lombard, W.A. Nexus between Social Vulnerability and Resilience to Agricultural Drought amongst South African Smallholder Livestock Households. Atmosphere 2023, 14, 900. [Google Scholar] [CrossRef]
  37. Khan, N.; Ma, J.; Kassem, H.S.; Kazim, R.; Ray, R.L.; Ihtisham, M.; Zhang, S. Rural Farmers’ Cognition and Climate Change Adaptation Impact on Cash Crop Productivity: Evidence from a Recent Study. Int. J. Environ. Res. Public Health 2022, 19, 12556. [Google Scholar] [CrossRef]
  38. Jha, S.; Kaechele, H.; Lana, M.; Amjath-Babu, T.S.; Sieber, S. Exploring farmers’ perceptions of Agricultural Technologies: A Case Study from Tanzania. Sustainability 2020, 12, 998. [Google Scholar] [CrossRef]
  39. Ahmed, Z.; Shew, A.M.; Mondal, M.K.; Yadav, S.; Jagadish, S.V.; Prasad, P.V.; Buisson, M.C.; Das, M.; Bakuluzzaman, M. Climate risk perceptions and perceived yield loss increases agricultural technology adoption in the polder areas of Bangladesh. J. Rural Stud. 2022, 94, 274–286. [Google Scholar] [CrossRef]
  40. Aliyar, Q.; Zulfiqar, F.; Datta, A.; Kuwornu, J.K.M.; Shrestha, S. Drought perception and field-level adaptation strategies of farming households in drought-prone areas of Afghanistan. Int. J. Disaster Risk Reduct. 2022, 72, 102862. [Google Scholar] [CrossRef]
  41. Gashure, S.; Wana, D. Smallholder farmers’ perceptions, coping and adaptation strategies to climate variability in the UNESCO designated cultural landscapes of Konso. Ethiopia 2023, 28, 1243–1262. [Google Scholar] [CrossRef]
  42. Marie, M.; Yirga, F.; Haile, M.; Tquabo, F. Farmers’ choices and factors affecting adoption of climate change adaptation strategies: Evidence from northwestern Ethiopia. Heliyon 2020, 6, e03867. [Google Scholar] [CrossRef]
  43. Sertse, S.F.; Khan, N.A.; Shah, A.A.; Liu, Y.; Naqvi, S.A.A. Farm households’ perceptions and adaptation strategies to climate change risks and their determinants: Evidence from Raya Azebo district, Ethiopia. Int. J. Disaster Risk Reduct. 2021, 60, 102255. [Google Scholar] [CrossRef]
  44. Olivares, B.O. Análisis temporal de la sequía meteorológica en localidades semiáridas de Venezuela. UGCiencia 2017, 22, 11–24. [Google Scholar] [CrossRef]
  45. Ortega-Gaucin, D.; Velasco, I. Aspectos socioeconómicos y ambientales de las sequías en México. Aqua-LAC 2013, 5, 78–90. [Google Scholar] [CrossRef]
  46. Melo-Leon, S.F.; Riveros-Salcedo, L.C.; Romero-Otalora, G.; Álvarez, A.C.; Diaz-Giraldo, C.; Calderón-Diaz, S.L. Efectos Económicos de Futuras Sequías en Colombia: Estimación a Partir del Fenómeno El Niño 2015. 2017. Available online: https://colaboracion.dnp.gov.co/CDT/Estudios%20Econmicos/466.pdf (accessed on 15 February 2024).
  47. Li, J.; Huimin, L. Impacts of climate change on winter wheat and summer maize dual-cropping system in the North China Plain. Environ. Res. Commun. 2022, 4, 075014. [Google Scholar] [CrossRef]
  48. Yao, P.; Qian, L.; Wang, Z.; Meng, H.; Ju, X. Assessing Drought, Flood, and High Temperature Disasters during Sugarcane Growth Stages in Southern China. Agriculture 2022, 12, 2117. [Google Scholar] [CrossRef]
  49. Musafiri, C.M.; Macharia, J.M.; Ng’etich, O.K.; Kiboi, M.N.; Okeyo, J.; Shisanya, C.A.; Okwuosa, E.A.; Mugendi, D.N.; Ngetich, F.K. Farming systems’ typologies analysis to inform agricultural greenhouse gas emissions potential from smallholder rain-fed farms in Kenya. Sci. Afr. 2020, 8, e00458. [Google Scholar] [CrossRef]
  50. Asrat, P.; Simane, B. Household- and plot-level impacts of sustainable land management practices in the face of climate variability and change: Empirical evidence from Dabus Sub-basin, Blue Nile River, Ethiopia. Agric. Food Secur. 2017, 6, 61. [Google Scholar] [CrossRef]
  51. Bwambale, N. Farmers’ Knowledge, Perceptions, and Socioeconomic Factors Influencing Decision Making for Integrated Soil Fertility Management Practices in Masaka and Rakai Districts, Central Uganda. Master’s Thesis, Iowa State University, Iowa, IA, USA, 2015. [Google Scholar]
  52. Al Mamun, A.; Roy, S.; Islam, A.R.M.; Alam, G.M.; Alam, E.; Chandra Pal, S.; Sattar, A.; Mallick, J. Smallholder farmers’ perceived climate-related risk, impact, and their choices of sustainable adaptation strategies. Sustainability 2021, 13, 11922. [Google Scholar] [CrossRef]
  53. Rojas-Tiempo, J.; Díaz-Ruiz, R.; Álvarez-Gaxiola, F.; Ocampo-Mendoza, J.; Escalante-Estrada, A. Tecnología de producción de haba y características socioeconómicas de productores en Puebla y Tlaxcala. Rev. Mex. Cienc. Agríc. 2018, 3, 35–49. [Google Scholar] [CrossRef]
  54. OMM. Guide to meteorological instruments and methods of observation. In World Meteorological Organization: Vol. I (Issue 8); WMO-No. 8: Measurement of Meteorological Variables; 2018. Available online: https://community.wmo.int/en/activity-areas/imop/wmo-no_8 (accessed on 19 June 2024).
  55. UNGRD; IDEAM; MINAMBIENTE; CANCILLERÍA. Estrategia Nacional para la Gestión Integral de la Sequía en Colombia. 2018. Available online: https://faolex.fao.org/docs/pdf/col218772.pdf (accessed on 19 February 2024).
  56. IGAC. Estudio General de Suelos y Zonificación de Tierras Departamento del Tolima Escala 1: 100.000, 1st ed.; In Instituto Geográfico Agustín Codazzi: Bogota, Colombia, 2016; pp. 1–545. [Google Scholar]
  57. CORTOLIMA. Plan de Gestión Ambiental Regional del Tolima 2013–2023. [Internet]; Corporación Autónoma Regional del Tolima: Ibagué, Colombia, 2009. Available online: https://tolima.gov.co/images/transparencia/planeacion/formatos-de-empalme/gestion-del-desarrollo/lineamientos-seguimiento-plan-de-desarrollo/planes-sectoriales/gestion-del-riesgo/plan-de-gestion-ambiental-regional-2013_2023_tolima.pdf (accessed on 20 February 2024).
  58. Anning, A.K.; Ofori-Yeboah, A.; Baffour-Ata, F.; Owusu, G. Climate change manifestations and adaptations in cocoa farms: Perspectives of smallholder farmers in the Adansi South District, Ghana. Curr. Res. Environ. Sustain. 2022, 4, 100196. [Google Scholar] [CrossRef]
  59. Puerta-Cortés, D.; Carbonell, X. El modelo de los cinco grandes factores de personalidad y el uso problemático de Internet en jóvenes colombianos. Adicciones 2014, 26, 54. [Google Scholar] [CrossRef] [PubMed]
  60. Dendir, Z.; Simane, B. Farmers’ perceptions about changes in climate variables: Perceived risks and household responses in different agro-ecological communities, Southern Ethiopia. Clim. Serv. 2021, 22, 100236. [Google Scholar] [CrossRef]
  61. Yiridomoh, G.Y.; Appiah, D.O.; Owusu, V.; Bonye, S.Z. Women smallholder farmers off-farm adaptation strategies to climate variability in rural Savannah, Ghana. GeoJournal 2021, 86, 2367–2385. [Google Scholar] [CrossRef]
  62. McCoach, D.B.; Gable, R.K.; Madura, J.P. Instrument Development in the Affective Domain; Springer: New York, NY, USA, 2013; Volume 10, pp. 978–981. [Google Scholar]
  63. Mooney, S.; O’Dwyer, J.; Lavallee, S.; Hynds, P.D. Private groundwater contamination and extreme weather events: The role of demographics, experience and cognitive factors on risk perceptions of Irish private well users. Sci. Total Environ. 2021, 784, 147118. [Google Scholar] [CrossRef] [PubMed]
  64. Ayeri, O.S.; Christian, V.R.; Josef, E.; Michael, H. Local Perceptions and Responses to Climate Change and Variability: The Case of Laikipia District, Kenya. Sustainability 2012, 4, 3302–3325. [Google Scholar] [CrossRef]
  65. Mekonnen, M.; Abeje, T.; Addisu, S. Integrated watershed management on soil quality, crop productivity and climate change adaptation, dry highland of Northeast Ethiopia. Agric. Syst. 2021, 186, 102964. [Google Scholar] [CrossRef]
  66. Ngure, M.W.; Wandiga, S.O.; Olago, D.O.; Oriaso, S.O. Climate change stressors affecting household food security among Kimandi-Wanyaga smallholder farmers in Murang’a County, Kenya. Open Agric. 2021, 6, 587–608. [Google Scholar] [CrossRef]
  67. Jia, J.J.; Wang, Y.; Liu, X.S.; Li, F.; Zhang, X.S. The response capability analysis of farmers on social-economic-natural complex ecosystem management in Alxa, Inner Mongolia. Shengtai Xuebao 2017, 37, 5836–5845. [Google Scholar] [CrossRef]
  68. Maliki, M.A.; Pauline, N.M. Living and Responding to Climatic Stresses: Perspectives from Smallholder Farmers in Hanang’ District, Tanzania. Environ. Manag. 2022, 71, 131–144. [Google Scholar] [CrossRef]
  69. Von-Gehren, P.; Bomers, S.; Tripolt, T.; Söllinger, J.; Prat, N.; Redondo, B.; Vorss, R.; Teige, M.; Kamptner, A.; Ribarits, A. Farmers Feel the Climate Change: Variety Choice as an Adaptation Strategy of European Potato Farmers. Climate 2023, 11, 189. [Google Scholar] [CrossRef]
  70. Darjee, K.B.; Neupane, P.R.; Köhl, M. Proactive Adaptation Responses by Vulnerable Communities to Climate Change Impacts. Sustainability 2023, 15, 10952. [Google Scholar] [CrossRef]
  71. Weber, E.U.; Stern, P.C. Public Understanding of Climate Change in the United States. Am. Psychol. 2011, 66, 315–328. [Google Scholar] [CrossRef] [PubMed]
  72. Smit, B.; Pilifosova, O. Adaptation to Climate Change in the Context of Sustainable Development and Equity. In Climate Change 2003: Impacs, Adaption, and Vulnerability; McCarthy, J.J., Canciany, O.F., Leary, N.A., Dokken, D.J., White, K.S., Eds.; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2001; p. 1022. [Google Scholar]
  73. Ayanlade, A.; Oluwatimilehin, I.A.; Ayanlade, O.S.; Adeyeye, O.; Abatemi-Usman, S. Gendered vulnerabilities to climate change and farmers’ adaptation responses in Kwara and Nassarawa States, Nigeria. Humanit. Soc. Sci. Commun. 2023, 10, 911. [Google Scholar] [CrossRef]
  74. Islam, M.S.; Kabir, M.H.; Ali, M.S.; Sultana, M.S.; Mahasin, M. Farmers’ Knowledge on Climate Change Effects in Agriculture. Agric. Sci. 2019, 10, 386–394. [Google Scholar] [CrossRef]
  75. Landaverde, R.; Rodriguez, M.T.; Niewoehner-Green, J.; Kitchel, T.; Chuquillanqui, J. Climate Change Perceptions and Adaptation Strategies: A Mixed Methods Study with Subsistence Farmers in Rural Peru. Sustainability 2022, 14, 6015. [Google Scholar] [CrossRef]
  76. Smit, B.; Pilifosova, O. From adaptation to adaptive capacity and vulnerability reduction. In Climate Change, Adaptive Capacity and Development; World Scientific: Singapore, 2003; pp. 9–28. [Google Scholar] [CrossRef]
  77. Mensah, M.; Vlek, P.L.G.; Fosu-Mensah, B.Y. Gender and climate change linkages in the semi-arid region of Ghana. GeoJournal 2022, 87, 363–376. [Google Scholar] [CrossRef]
  78. Ahmed, Z.; Guha, G.S.; Shew, A.M.; Alam, G.M.M. Climate change risk perceptions and agricultural adaptation strategies in vulnerable riverine char islands of Bangladesh. Land Use Policy 2021, 103, 105295. [Google Scholar] [CrossRef]
  79. Hossain, M.F. Impact of climate change in Bangladesh: Rainfall. Int. J. Agric. Innov. Res. 2014, 2, 860–863. [Google Scholar]
  80. Karki, S.; Burton, P.; Mackey, B. The experiences and perceptions of farmers about the impacts of climate change and variability on crop production: A review. Clim. Dev. 2020, 12, 80–95. [Google Scholar] [CrossRef]
  81. Waibel, H.; Pahlisch, T.H.; Völker, M. Farmers’ Perceptions of and Adaptations to Climate Change in Southeast Asia: The Case Study from Thailand and Vietnam. Clim. Smart Agric. Build. Resil. Clim. Chang. 2018, 52, 137–160. [Google Scholar] [CrossRef]
  82. Alam, G.M.M.; Alam, K.; Mushtaq, S. Climate change perceptions and local adaptation strategies of hazard-prone rural households in Bangladesh. Clim. Risk Manag. 2017, 17, 52–63. [Google Scholar] [CrossRef]
  83. Habiba, U.; Shaw, R.; Takeuchi, Y. Farmer’s perception and adaptation practices to cope with drought: Perspectives from Northwestern Bangladesh. Int. J. Disaster Risk Reduct. 2012, 1, 72–84. [Google Scholar] [CrossRef]
  84. Sattler, D.N.; Bishkhorloo, B.; Graham, J.M. Climate change threatens nomadic herding in Mongolia: A model of climate change risk perception and behavioral adaptation. J. Environ. Psychol. 2021, 75, 101620. [Google Scholar] [CrossRef]
  85. Iglesias, A.; Garrote, L.; Bardají, I.; Santillán, D.; Esteve, P. Looking into individual choices and local realities to define adaptation options to drought and climate change. J. Environ. Manag. 2021, 293, 112861. [Google Scholar] [CrossRef]
  86. Suazo Muñoz, C.; Sandoval-Díaz, J. Revisión sistemática de la percepción de riesgo en agricultores ante la sequía: Factores de influencia, contenidos percibidos, estrategias de adaptación y prácticas asociadas. Sci. Agropecu. 2023, 14, 139–152. [Google Scholar] [CrossRef]
  87. Núñez, E.G.; Lara, S.C.; García, I.R. Vulnerabilidad social y percepción del riesgo en la población cubana expuesta a la sequía. Pers. Y Soc. 2022, 36, 9–28. [Google Scholar] [CrossRef]
Sustainability 16 07210 g001
Figure 1. Study area in the dry zone of the department of Tolima, Colombia.
Figure 1. Study area in the dry zone of the department of Tolima, Colombia.
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Figure 2. Lifestyle of farmers in the dry zone of the department of Tolima, Colombia.
Figure 2. Lifestyle of farmers in the dry zone of the department of Tolima, Colombia.
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Table 1. Socio-demographic variables of farmers in the dry zone of the department of Tolima, Colombia.
Table 1. Socio-demographic variables of farmers in the dry zone of the department of Tolima, Colombia.
GroupIndividualPercentage (%)
Marital statusSingle21.0
Married36.1
Widower5.6
Civil Union35.5
Divorced1.9
Household members1.07.2
1 a 237.0
3 a 440.7
5>15.1
ChildrenYes80.1
No19.9
Dependents021.5
124.2
229.7
≥324.5
Monthly income **<1 million41.1
1 million29.0
1 a 2 million18.2
2 a 3 million5.8
4 a 5 million2.4
≥5 million3.5
HousingOwn62.3
Rented12.1
Family25.7
Time in housing<1 year3.0
1 to 2 years10.4
3 to 4 years12.8
>5 years73.8
Cooking withNatural gas30.5
Firewood27.7
Gas cylinder14.7
Natural Gas + Firewood1.1
Natural Gas + Gas cylinder1.1
Firewood + Gas cylinder24.9
Main source of incomeLivestock4.5
Agriculture82.7
Fish farming3.0
Trade4.8
Other1.7
Livestock + Agriculture3.2
Trade + Agriculture0.2
Note: ** 1.000.000 COP = 254 USD.
Table 2. Correlations between age, risk perception, socioeconomic vulnerability, and drought adaptation categories.
Table 2. Correlations between age, risk perception, socioeconomic vulnerability, and drought adaptation categories.
VariablesM (Pd) SDASATSVTSRTP
1. Age (A)48.1714.751
2. Adaptation to droughts (SAT)31.176.93−0.107 *1
3. Socio-economic vulnerability (SVT)37.316.63−0.148 **0.540 **1
4. Perception of risk to droughts (SRPT)76.1914.20−0.630.367 **0.489 **1
M: Mean; SD: Standard deviation; * Significant correlation at 0.05; ** Significant correlation at 0.01.
Table 3. Binary logistic regression analysis between the categories age, risk perception, socioeconomic vulnerability and adaptation to drought.
Table 3. Binary logistic regression analysis between the categories age, risk perception, socioeconomic vulnerability and adaptation to drought.
VariablesBSDBETAtSig.CI 95%
Age−0.010.02−0.03−0.7960.427−0.050.02
Socio-economic vulnerability (SAT)0.4910.040.46911.25<0.0010.4050.58
Perception of risk to droughts (SRPT)0.0660.020.1353.2680.0010.0260.11
B: Non-standard coefficient; SD: Standard deviation; BETA: Standard coefficient; t: Lower limit; Sig.: Upper limit; CI: Confidence interval.
Table 4. Results of the questionnaires and also each dimension of the SPRT.
Table 4. Results of the questionnaires and also each dimension of the SPRT.
Study QuestionnairesResults
Adaptation to drought On average, participants are adjusting to different drought adaptation measures, identifying new forms of water storage, improving agricultural techniques, and seeking other activities to improve income.
Socioeconomic vulnerability In general, participants have a medium level of socio-economic vulnerability, indicating that some participants live in inequitable social contexts that make them more susceptible to droughts. However, despite the circumstances, they have water for farming and household activities, electricity, and access to health care.
Drought risk perceptionDrought risk perception: On average, participants have the ability to detect, identify, and react to droughts, which may be influenced by the situations and their experiences. They are concerned about droughts and their consequences.
Dimension perceived probability. For the most part, participants have a perceived probability at the medium level, indicating that not all significantly establish future experience of the event, observation of changes prior to the event, and adoption of the drought event.
Dimension perceived severity. Most of the participants have a medium perceived probability, which indicates that the perception of the impact of agricultural droughts is not detected as serious.
Dimension perceived consequences. Most of the participants have a perceived probability of the consequences of droughts at a medium level, in relation to what they cause in crop productivity. This is possibly due to already-developed adaptation mechanisms.
Table 5. Comparison of variables used in the study with other research.
Table 5. Comparison of variables used in the study with other research.
AuthorCountryVariableResults
[35]PakistanFarmers’ risk perception
adaptation measures to climate change.		Age, education, farm size, household size, accessibility to credit, annual income, and perception of increasing temperature and decreasing precipitation significantly influenced the selection of adaptation strategies
[84]MongoliaClimate change risk perceptionClimate change knowledge was associated with a positive stress response, which in turn was associated with perceived climate change risk.
[85]SpainAdaptation to drought
Risk perception		Local experience is linked to risk perception, but does not necessarily drive adaptive behavior
[83]BangladeshDrought threat perception
Adaptation to drought		Agriculture, as well as the social life and health of farmers, are the most threatened.
Farmer-owners are more capable of adopting new technologies than tenant farmers.
[86]ChileRisk perception of droughtsFactors influencing the perception of drought (years of experience, level of schooling, etc.) and adaptation strategies (farm size, gender, access to credit, etc.)
[87]CubaDrought hazard perceptionThe population’s perception of drought risk is medium
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Hernández-López, J.A.; Puerta-Cortés, D.X.; Andrade, H.J. Predictive Analysis of Adaptation to Drought of Farmers in the Central Zone of Colombia. Sustainability 2024, 16, 7210. https://doi.org/10.3390/su16167210

AMA Style

Hernández-López JA, Puerta-Cortés DX, Andrade HJ. Predictive Analysis of Adaptation to Drought of Farmers in the Central Zone of Colombia. Sustainability. 2024; 16(16):7210. https://doi.org/10.3390/su16167210

Chicago/Turabian Style

Hernández-López, Jorge Armando, Diana Ximena Puerta-Cortés, and Hernán J. Andrade. 2024. "Predictive Analysis of Adaptation to Drought of Farmers in the Central Zone of Colombia" Sustainability 16, no. 16: 7210. https://doi.org/10.3390/su16167210

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