Knowledge, attitudes, and practices of banana farm workers regarding water management and associated factors

ABSTRACT

Banana production is an agricultural activity that requires intensive use of water. Its increasing demand brings about not only risks of water scarcity in banana-producing farms but also management challenges for the farm workers. Thus, understanding the association of water management practices with the banana workers’ knowledge, attitude, and practices is crucial. Therefore, this study aimed to develop a scale to assess the relationship among the knowledge, attitudes, and practices – KAP – of banana farm workers regarding water management and associated factors. A cross-sectional descriptive study was conducted on three banana farms in the Urabá region, Colombia. The scale showed excellent psychometric properties in the dimensions of knowledge and attitude. Scores on the scale indicated that the workers have a satisfactory level of knowledge and attitude and an excellent level of practice. However, no relationship was noted between practices, and knowledge and attitude. The results suggest that workers’ practices do not depend on their knowledge and attitude as they are workers employed by one company. Thus, the practices of farm workers can be easily controlled as they have to do what they are told to do by their boss.

KEYWORDS:

• KAP model
• sociodemographics
• water management
• banana production
• sustainability

Introduction

Banana is one of the main global crops in agricultural trade (FAO, 2020). Banana production has rapidly increased in recent decades because of growing global demand and the increasing population in countries that produce bananas (FAO, 2020). The average global banana production reportedly increased from 69 million tons in 2000–2002 to 116 million tons in 2017–2019 (FAO, 2020). The exporting countries with the highest production of bananas are Ecuador, the Philippines, Brazil, India, China, Guatemala, the Dominican Republic, Colombia, and Costa Rica (FAO, 2020). In Colombia, the Urabá region is one of the main production areas where big companies are involved in the production of bananas (Toro-Trujillo et al., 2016).

This trend of increasing banana production may entail several environmental threats related to water management because of the exacerbation of the typical environmental effects of this industry. A substantial amount of water is used during the banana production cycles such as planting, harvesting, postharvest activities, and particularly, fruit washing (FAO, 2017; Oliveira et al., 2022; Panigrahi et al., 2021). Water quality is principally affected by fruit washing residues (e.g. latex, crown fragments, fungicides, and alum) and their final disposal into water bodies, sometimes without any type of treatment (Geissen et al., 2010; Svensson et al., 2018). Thus, an increase in banana production can lead to water scarcity and pollution in the producing regions.

One way to face these anticipated threats is the realization of changes in the agricultural practices and, therefore, in the behaviour of those who work in the banana production farms. In this context, understanding the relationship among the knowledge, attitudes, and practices – KAP – of the workers is particularly relevant. However, the number of studies that have explored this topic is limited. On the one hand, the KAP studies carried out in banana production systems have been focused on farmers, do not use KAP scales and do not specifically address water management. Kayat et al. (2016) used a KAP model to study the intention of banana growers to improve production. They identified a significant relationship between the banana growers’ KAP and the farm’s productivity. This study reported some psychometric properties of the scale used; however, the scope of their study was banana production but not water management. Other study (Oremo et al., 2019) developed with farmers used a KAP scale related to water resource management, but it was conducted in a system other than that of banana. On the other hand, KAP studies carried out with workers have been reported in the fields of the meat industry (Jorga et al., 2022), fishery (Agüeria et al., 2018), fruit farms (Weng & Black, 2015) and live poultry markets (Lei et al., 2019). None of them were carried out with banana farm workers nor used KAP scales related to water management.

Studies using KAP scales are particularly relevant to this case because they are representative for specific populations. They allow the researcher to collect data on what is known, believed, and followed regarding a particular topic (Emanuel, 2010). Knowledge refers to the information that people gain, retain, and use (Wan et al., 2016), as well as their ideas regarding the matter (Gumucio et al., 2011). Attitude represents their stances, manner of judgment and evaluation, and tendencies to act in a particular manner in response to the object of the attitude (Gumucio et al., 2011; Wan et al., 2016). Practices are the observable set of actions of an individual (Gumucio et al., 2011; Wan et al., 2016).

A basic assumption of the KAP scale is that the relevant domains are three constructs that influence one another (Wan et al., 2016). Studies using KAP scales can be valuable tools as they evaluate the degree of people’s developmental skills and help to identify issues where it is necessary to improve the awareness and education of the target population and the obstacles that they face in improving their practices (Gumucio et al., 2011). Thus, KAP studies have become a basic approach for the development of solid arguments that contribute to the formulation, design, and implementation of agricultural management strategies (Mior et al., 2016). The present study aims to build a scale to analyse the relationship between banana farm workers’ KAP regarding water management and the sociodemographic factors that can influence these three components.

Participants and methods

Study design

This was a descriptive, cross-sectional study.

Study farms

Because this study attempts to develop a KAP scale regarding water management, the farms were selected by considering the variety of management systems of water cycle that the region may have. This would allow capturing a wider diversity of workers in terms of the variety of situations through which they can make contact with water on a farm. This also contributes to improving the validity of the scale. Thus, three farms were selected. Farm 1 draws water from a deep well. Farm 2 does not only capture water from a deep well, but also collects rainwater, and Farm 3 collects water from a surface source (a river). These farms are described in Table 1.

Table 1. Characteristics of the farms under study.

Farm	Distance from headquarters (kms)^a	Land area (ha)	Net cultivated area (ha)	Average banana production (kg/month)	Monthly yield (kg/ha)	Total workers
Farm 1	20.7	300.64	254.47	941,825.67	3701.13	205
Farm2	19.3	149.41	119.36	475,231.50	3981.50	120
Farm 3	13.7	100.65	81.81	340,638.33	4163.77	87

^a Google Maps estimates.

Study participants

Two hundred workers from three banana farms in the Urabá region (Colombia) were enrolled in this study. Of them, 82 workers were from farm 1, 64 from farm 2, and 54 from farm 3. All workers belong to the same company. The three farms were selected by the concerned company based on the logistical feasibility of data collection. The sample size was calculated assuming a confidence level of 95%, a reference population of 412 workers, a standard deviation of 15 in the scores on each KAP dimension, and a precision of 2. A stratified sampling was performed with proportionate allocation per farm. Two inclusion criteria were applied to select the participants: field staff and packers, and field and packing house supervisors of any sex or socioeconomic stratum. Workers who were not interested in participating were excluded. The participants’ consent was obtained after giving them full information on the research and before the application of the survey.

A survey was conducted among the workers of three farms. Participants were selected according to their availability during break time (breakfast and lunch) and based on voluntary participation. When logistical conditions were challenging, participants were selected by their supervisors. To avoid information bias, the survey was self-administered. During the survey, participants were guaranteed anonymity and confidentiality of the information they provided.

Likert scale

A Likert scale with four modules was developed. The first module included questions on the sociodemographics of the study population. The remaining three modules corresponded to the dimensions of KAP regarding water management. The scale was developed in three stages. In the first stage, different bibliographic sources were reviewed (Ashoori et al., 2016; Oremo et al., 2019; Senasa, 2020) to identify the items that would constitute the scale. In the second stage, four individuals with experience in banana cultivation were interviewed to validate the scale. In the third stage, a pilot test was performed with nine banana farm workers to evaluate the requirement for any possible modification, addition, or removal of scale items. The definitive survey questions used are presented in Appendix 1.

Data analysis

The sociodemographic characteristics of the participants – first module – were described using frequencies and summary statistics. Question 9 (what activities do you perform on the farm?) was transformed into a binary variable by classifying the open responses into two categories: (1) packing house and/or field work and (2) administrative assistance. The first category includes field work activities (farming, leaf stripping, population control of plants, bagging, fruit mooring, fumigation, garruchero, gauntelete, hércules, palero¹, and other functions), and packing house work (e.g. cardboard work, selection, classification, and sealing) (Senasa, 2020). These activities are performed during the phases of planting, harvesting, postharvest activities, and fruit washing. That is the reason why the use of water by this type of workers differs from that of administrative ones. The second category includes supervision and other administrative activities.

To describe the KAP profile, an index was designed for each dimension of KAP. Each index had a score ranging from 0 (lowest) to 100 (highest) and was calculated using the following formula: [(Sum of domain items − minimum score)/rank] × 100. This three-index profile was described using summary statistics.

A Kolmogorov–Smirnov test was used to verify the normality assumption for each variable. Since the results revealed non-normal distributions, non-parametric tests were performed to evaluate the relationship between the workers’ KAP profile and sociodemographics. Spearman’s Rho, Mann–Whitney U, and Kruskal–Wallis H tests were performed. In these analyses, the statistical significance was set at 0.01 and 0.05. Statistically significant relationships were evaluated using multivariate linear regression analysis. The analyses were performed with Statistical Package for the Social Sciences for Windows software SPSS® version 27.

The psychometric properties of the scale were initially assessed to remove the items that could affect its validity. Reliability accounts for degree of variance attributable to the existing differences among the participants. It was evaluated using Cronbach’s alpha, and values higher than 0.70 were considered satisfactory (λ > 0.7). Internal consistency is the degree of the interrelation between the items of one domain and the domain they belong to. Spearman’s correlations were used and values higher than 0.30 were considered favourable. Discriminatory power was assumed as satisfactory when the Spearman coefficient was greater in the item–dimension correlations to which it belongs compared to the item–dimension correlations to which it does not belong. A difference of 0.20 between both coefficients was considered acceptable. Content validity was evaluated using the λ coefficients of the exploratory factor analysis. Lambda coefficients above 0.30 were considered acceptable. The predictive validity was evaluated using the proportion of the explained variance – of the exploratory factor analysis – of each dimension of KAP (Luján-Tangarife & Cardona-Arias, 2015). After removing some items, the psychometric properties were re-evaluated; then, the analyses were performed.

Results

Psychometric properties of the scale

Several items were removed based on the results of the initial assessment of the psychometric properties of the scale (Table 2). In the knowledge dimension, the item Excessive banana planting degrades soil and reduces water availability was removed because it did not show satisfactory discriminatory power nor an acceptable content validity (λ = 0.24). Furthermore, the item For fruit washing, water must be treated was also removed as it did not show satisfactory discrimination power. In the attitude dimension, the items My participation in decision-making on water management is … and Drinking water must be used when washing the fruit were removed; the first one was removed because it did not show satisfactory discriminatory power and the latter for not showing satisfactory content validity (λ = 0.25). In the practice dimension, the item I attend trainings on water management in agricultural processes and/or banana production was removed because of not showing satisfactory content validity (λ = 0.26). Moreover, the item When I wash my hands, I turn off the tap while soaping my hands was removed for not showing satisfactory internal consistency (Rho = 0.27) nor content validity (λ = 0.095).

Table 2. Analysis of the psychometric properties of the KAP scale.

	Knowledge		Attitudes		Practices
	Initial	Final	Initial	Final	Initial	Final
Reliability
Cronbach’s alpha	0.830	0.835	0.849	0.851	0.457	0.475
Internal consistency
Spearman’s Rho rank	0.36–0.73	0.60–0.75	0.44–0.67	0.57–0.71	0.28–0.60	0.46–0.78
Success % (Rho ≥ 0.30)	100 (11/11)	100 (9/9)	100 (16/16)	100 (14/14)	85 (6/7)	100(5/5)
Discriminatory power
Spearman’s Rho rank	−0.12–0.47	−0.16–0.42	−0.16–0.39	−0.16–0.35	−0.12–0.14	−0.18–0.13
Success % (Rho ≥ 0.20)	90 (20/22)	100 (18/18)	97 (31/32)	100 (28/28)	100 (14/14)	100 (10/10)
Content validity
Rank coefficient λ	0.34–0.55	0.46–0.79	0.25–0.77	0.40–0.77	0.10–0.83	0.27–0.87
Success % (λ ≥ 0.30)	91 (10/11)	100 (9/9)	94 (15/16)	100 (32/32)	74 (5/7)	80 (4/5)
Predictive validity
Variance explained (%)	37	44	35	38	27	38

Once the abovementioned items were removed, the evaluation of the scale showed excellent psychometric properties for the dimensions of knowledge and attitude but not for that of practice (Table 2). The final scale had excellent reliability for the knowledge and attitude dimensions; however, the reliability was somewhat low for the practice dimension, for which the Cronbach’s alpha was < 0.7. The final scale had excellent internal consistency and discrimination power for all three dimensions. The content validity was excellent for the dimensions of knowledge and attitude but not practice, where the percentage of success reached 80%. Predictive validity also improved for all three dimensions after item removal.

Sociodemographics of the workers

Analysis of the workers’ sociodemographics (Table 3) revealed that they had an average age of 41 (±11.78) years and lived in households with an average number of 4 (±1.67) people. Most workers were men (86.5%) and received education equivalent to basic secondary education or less (80%), lived in urban areas (77.5%), belonged to the socioeconomic stratum 1 or 2 (98%), and were residents of the municipality of Apartadó or Turbo (71.5%). The group of participants had an average of 14.66 (±11.12) years of experience in banana farms. Most of the workers performed activities related to packing, crop management, and fruit reception and characterization (94%). Furthermore, 83% of the company workers used water while performing activities within the company; 41% of the participants worked on farm 1, 32% on farm 2, and 27% on farm 3.

Table 3. Sociodemographic characteristics of the workers.

Variable	Category	n	%
Sex	Woman	27	13.5
	Man	173	86.5
Level of education	Nonschooling	7	3.5
	Primary education	49	24.5
	Secondary education	108	54.0
	Technical/Technological	36	18.0
Home location	Rural area	45	22.5
	Urban area	155	77.5
Socioeconomic stratum	1	132	66.0
	2	65	32.5
	3	3	1.5
Home municipality	Apartadó	79	39.5
	Carepa	30	15.0
	Chigorodó	27	13.5
	Turbo	64	32.0
Farm place	Farm 1	82	41.0
	Farm 2	64	32.0
	Farm 3	54	27.0
Farm activity	Packing house, Field or Fruit reception	188	94.0
	Administrative assistance	12	6.0
Use of water in farm activities	Yes	166	83.0
	No	34	17.0
Variable	X ± SD	Median	Rank
Age	40.05 ± 11.78	39.00	19–66
Number of people living at home	3.94 ± 1.67	4.00	1–11
Years of experience	14.66 ± 11.12	12.00	0.5–42

X, mean; SD, standard deviation.

Levels of KAP

Table 4 presents the KAP scores. Considering that a score of 0–60 indicates average/moderate, 61–80 good, and 81–100 excellent KAP, it can be affirmed that the workers have, on average, a satisfactory level of knowledge (68.59) and attitude (76.4) and an excellent level of practice (82.14). The results for each item are presented in Appendix 2.

Table 4. Level of knowledge, attitudes, and practices of the farm workers regarding water management.

Variable^a	X ± SD	Median	Rank
Level of knowledge	70.56 ± 18.99	66.7	11.11–100
Level of attitudes	77.14 ± 13.33	76.2	38.10–100
Level of practices	82.50 ± 16.88	86.7	6.67–100

X, mean; SD, standard deviation.

^a Score between 0 and 100.

Sociodemographic factors associated with the KAP profile

The bivariate analysis (Table 5) revealed that the workers’ knowledge and attitudes are correlated but their practices are not related to knowledge or attitude. In the knowledge dimension, the results showed that this score increases when the level of education rises, and is higher in administrative workers as well as in farm 2 workers. The relationship with the municipality of residence was only significant at a confidence level of 90%. In the attitude dimension, this score increases when the educational level increases, being higher for workers who use water daily, and lower for the farm 3 workers. Regarding the practice dimension, the score increases with age and years of experience and decreases with the level of education. Workers who lived in rural areas and farm 3 workers had higher scores in the practice domain than other workers. In contrast, no relationship was identified between the workers’ KAP profile and the number of people living at home with the workers or the workers’ sex and socioeconomic stratum.

Table 5. Knowledge, attitudes, and practices scores according to the farm workers’ sociodemographics.

Variable	Knowledge	Attitudes	Practices
Knowledge	1	0.451**	−0.047
Attitude	0.451**	1	−0.056
Practice	−0.047	−0.121	1
Age	−0.067	−0.111	0.373**
Number of people living at home	0.011	−0.002	0.036
Years of experience	0.001	−0.081	0.333**
Educational level	0.232**	0.230**	−0.203**
Sex (Me ± IR)
Woman	70.4 (59.3–92.6)	78.6 (66.7–90,5)	80.0 (73.3–93.3)
Man	66.7 (59.3–85.2)	76.2 (66.2–85.7)	86.7 (73.3–100)
Vp U M-W	0.903	0.696	0.380
Stratum (Me ± RI)
Stratum 1	66.7 (59.3–88.9)	76.2 (66.7–85.7)	86.7 (73.3–100)
Strata 2 and 3	66.7 (57.4–77.8)	78.6 (67.9–88.1)	80.0 (66.7–96.7)
Vp U M-W	0.420	0.364	0.227
Home location (Me ± IR)
Rural area	66.7 (59.3–88,9)	78.6 (66.3–88.1)	93.3 (80.0–100)
Urban area	66.7 (59.3–85.2)	76.2 (66.7–85.7)	80.0 (73.3–93.3)
Vp U M-W	0.414	0.637	0.027
Farm activity (Me ± IR)
Field/packing/fruit reception	66.7 (59.3–85.2)	76.2 (66.7–85.7)	86.7 (73.3–100)
Administrative assistance	87.0 (74.1–94.4)	81.0 (72.6–89.3)	80.0 (73.3–86.7)
Vp U M-W	0.028	0.314	0.204
Use of water (Me ± IR)
Yes	66.7 (59.3–88.9)	78.6 (66.7–88.1)	86.7 (73.3–100)
No	66.7 (59.3–81.5)	71.4 (64.3–78.6)	83.3 (73.3–100)
Vp U M-W	0.806	0.011	0.956
Home municipality (Me ± IR)
Apartadó	66.7 (59.3–85.2)	73.8 (66.7–83.3)	86.7 (73.3–100)
Carepa	68.5 (51.9–77.8)	77.4 (66.7–88.1)	80.0 (66.7–86.7)
Chigorodó	63.0 (55.6–70.4)	78.6 (73.8–88.1)	80.0 (73.3–86.7)
Turbo	66.7 (63.0–92.6)	78.6 (66.7–88.1)	90.0 (80.0–100)
Vp H de K-W	0.081	0.315	0.083
Farm (Me ± IR)
Farm 1	66.7 (51.9–77.8)	77.4 (66.7–88.1)	80.0 (66.7–93.3)
Farm 2	74.1 (64.8–96.3)	81.0 (69.1–88.1)	83.3 (73.3–100)
Farm 3	66.7 (59.3–85.2)	71.4 (64.3–81.0)	93.3 (80.0–100)
Vp H de K-W	0.006	0.012	0.000

Me, median; IR, interquartile range.

**Significant relationship on Spearman test at 0.05. Vp U M-W: p-value of U de Mann–Whitney test. Vp H de K-W: p-value of Kruskal–Wallis H test.

These relationships identified in the bivariate analysis were evaluated using linear regression models to identify spurious relationships (Table 6). The results showed that knowledge and attitude are related. The level of attitude was higher among the workers who used water and lower among farm 3 workers. Regarding the level of practice, the results of the regression analysis revealed that the score increased with age and was higher among the farm 3 workers. Notably, the variable level of education, which initially showed a relationship with the three dimensions, lacked statistical significance in the three regression models.

Table 6. Adjustment linear regression models for factors associated with the KAP scores.

Variable	Β	p-value	R^2
Model 1. Level of knowledge			0.263
Level of attitudes	0.591	0.000
Level of education	2.572	0.134
Activity within the farm	−7.891	0.134
Chigorodó	−5.682	0.117
Farm 2	4.835	0.070
Model 2. Level of attitudes			0.276
Level of knowledge	0.300	0.000
Level of education	2.031	0.073
Use of water	5.081	0.020
Farm 3	−5.307	0.004
Model 3. Level of practices			0.186
Age	0.386	0.024
Years of experience	0.074	0.677
Level of education	0.047	0.976
Rural area	4.601	0.089
Carepa	−1.749	0.579
Farm 3	7.769	0.003

B, regression coefficient; R², coefficient of determination.

Discussion

Characteristics of the banana farm workers

Regarding the sociodemographic characteristics of the workers, the results of this study show some similarities between these workers and those from other countries. The age of the workers surveyed in this study is 40.05 years old on average (± 11.78, median = 39.00) which is very close to the age of banana farm workers in countries like India (37.83, me = 42), Costa Rica (40.65, median = 40) and Indonesia (40.49, median = 47) (Pinedo, 2020). Regarding gender, most workers are men (86.5%). Even though this is similar to other countries, the share of men varies widely across countries like Costa Rica (84.6%), India (74.7%), Indonesia (67.5%) and Ethiopia (60%). In the case of education, there are also convergent findings.

The present study found that 54% of the workers received at least basic secondary education. The share of banana farm workers that have completed secondary education for Costa Rica, India and Indonesia is 51.9, 43.6 and 58.6%, respectively (Pinedo, 2020).

Factors associated with the workers’ KAP regarding water management

Knowledge and attitude were found not to affect the practices of the workers in the studied farms. Because the participants surveyed were from a single company, it is possible that their practices reflect the organizational norms rather than their own knowledge and attitude. This suggests that a suitable approach to influence workers’ practices may not consider their knowledge and attitude but their organizational environment. However, it should be noted that the level of practices was excellent. Some previous studies support this interpretation. In their study involving workers of a palm oil production company, Mior et al. (2016) found that the participants had a higher level of practices than that of knowledge and attitude, which was consistent with the findings of the present study. Fernández-Manzanal et al. (2015) reported that within an organization, the ability of individuals to implement practices according with their attitude and perception is influenced by the opportunities and constrains imposed by the organizational environment. Thus, the behaviour of workers may be influenced by organization’s procedure manuals, instructions, and guides. This might be the reason why no relationship between the workers’ practices and knowledge and attitude was identified in this study. However, the study of the influence of the organizational environment is beyond the boundaries of the subject matter of this research.

Regarding the factors associated with the workers’ KAP profile, it should be noted that the knowledge score is related to only the attitude score. This relationship is a common finding in KAP studies (Ovejero-Bernal, 2015). Workers who perform water-related activities have a more favourable attitude toward water management than others, which indicates that the connection with water promotes this attitude. On the other hand, a relationship between age and level of practices was also identified. This finding is consistent with the findings of other KAP studies indicating that older people perform more favourable practices toward sustainability than younger people (Salas-Zapata & Cardona-Arias, 2020).

Farm 3 represented an associated factor because it had particular features such as its workers had lower levels of attitude (although good) and a higher level of practices than the other workers. This fact could be explained by other equally distinctive characteristics of Farm 3. Among the three farms, for instance, Farm 3 is the smallest in terms of land, net cultivated area and number of workers, it is also the closest and the more productive farm (Table 1). The number of workers and the proximity to the company’s headquarters are probably two factors that make watching workers’ practices easier in this farm than in the others. However, this aspect requires additional studies.

Advantages of the scale

Compared with other KAP studies on water management in agricultural systems (Ashoori et al., 2016; Oremo et al., 2019), the present study reports a specifically designed scale to assess the workers’ KAP regarding water management in banana production farms. Generally, scientific publications in this field do not include the questionnaires used in the research. In this study, the applied scale has been attached as supplementary material (Appendix 1) to provide reference for other studies that aim to develop similar scales or use the present scale for the workers of different banana production farms.

Study scope and limitations

The evaluation of psychometric properties showed low reliability for the practice dimension, and no relationship was observed between the workers’ practices and the other two dimensions. This suggests that the scale can be used as a survey of three indices to determine factors associated with workers’ KAP, which represents the scope of the present study. However, it does not account for the psychometric construct of ‘KAP on water management’.

Because no relationship was identified between the workers’ practices and the other two dimensions, the assessment of the scale with small-scale banana farmers and the evaluation of the psychometric properties would be an interesting topic for future studies. This type of target population is freer on decision-making regarding the production system and is not conditioned by organizational rules. Under this scenario, the practices may better reflect the knowledge and attitude of the producers, and thus, the evaluation of psychometric properties may lead to different results. Notably, this study is representative of the population surveyed but not of the three farms because of logistical difficulties in randomizing the selection of participants.

Conclusions

Although the results of this study showed that workers from the three farms had satisfactory levels of knowledge and attitude and an excellent level of practice, this study revealed no correlation between the workers’ practices regarding water management and their knowledge and attitude. This study further suggests that the organizational environment that governs the activities of the workers has a considerable influence on their practices. Therefore, a possible pathway to influence the workers’ practices should involve the adjustment of their organizational environment. This study provides a scale that may serve as a foundation for the development of other scales with similar purposes.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Universidad Cooperativa de Colombia [Grant Number Proyectos en Alianza Inv2391, Inv2399]; Universidad de Antioquia [grant number Dir. de Regionalización y Escuela de Microbiologí­a].

Notes on contributors

Walter Alfredo Salas-Zapata

Walter Alfredo Salas-Zapata holds a PhD and is a researcher.

Ivis Tatiana Ramos-Hernández

Ivis Tatiana Ramos-Hernández holds a B.Sc and is a researcher.

Diana M. Gómez

Diana M. Gómez holds a MSc and is researcher.

Jesús Alonso Jaramillo-Arango

Jesús Alonso Jaramillo-Arango holds a MSc and is researcher.

Victor Hugo Aristizabal-Tique

Victor Hugo Aristizabal-Tique holds a PhD and is a researcher.

Francisco Javier Vélez-Hoyos

Francisco Javier Vélez-Hoyos holds a PhD and is a researcher.

Notes

1 Garruchero: worker who moves bunches of bananas whereby a cableway-pulley system. Guantelete: worker who places a plastic bag over the bunch to protect the fruits. Hércules: worker in charge of breaking the clod and bringing the soil. Palero: worker that clears and drains the channels in the plantation with a shovel.

Appendices

Appendix 1. Scale developed in this study

Workers’ knowledge, Attitude, and Practice (KAP) regarding water management in banana production

Age _____

Sex 1. Man__ 2. Woman__

Level of education:

1. Primary____ 2. Secondary____ 3. Technical/Technological___ 4. Nonschooling____

Home: 1. Rural area___ 2. Urban area___

Home stratum: 1 ___ 2___3___4___

Home municipality

1.Apartadó__ 2.Carepa__ 3.Chigorodó__ 4.Turbo__

How many people live at your home (including yourself)? ____

Farm where you work 1.Farm 1___ 2. Farm 2___ 3. Farm 3___

What is your job on the farm? ______________________

During your work, do you use water? 1. Yes__ 2. No__

How many years of experience do you have on banana farms?_____

Do you regularly receive information regarding the management of water? 1.Yes__ 2.No__

If your answer is yes, from which of the following sources do you receive the information?

1. Social media__ 2. Company__ 3. Neighbors/Family__ 4. Radio and television__ 5. Associations__ 6. Others__ Which?____________

Table

KNOWLEDGE
Please express your level of agreement/disagreement with the following statements	Options (mark the box with an X)
The degradation of forests impacts soil and decreases water availability	Disagree	Slightly agree	Agree	Strongly agree
Excessive banana planting degrades soil and reduces water availability	Disagree	Slightly agree	Agree	Strongly agree
I am aware of the sources that supply water to the farm for banana cultivation and washing	Disagree	Slightly agree	Agree	Strongly agree
Wastewater must be treated to use it for cultivation	Disagree	Slightly agree	Agree	Strongly agree
For fruit washing, water must be treated	Disagree	Slightly agree	Agree	Strongly agree
Treatment of the wastewater after banana washing reduces the pollution of the water bodies that receive the drainage	Disagree	Slightly agree	Agree	Strongly agree
I am aware of the water treatment system used for fruit washing in the packing house	Disagree	Slightly agree	Agree	Strongly agree
The recirculation of water in the packing house helps reduce water demand	Disagree	Slightly agree	Agree	Strongly agree
Water sources can be contaminated by the excessive use of agrochemicals	Disagree	Slightly agree	Agree	Strongly agree
I am aware of the stream or water body that receives the drainage from the crops	Disagree	Slightly agree	Agree	Strongly agree
I am aware of the drainage systems of the farm	Disagree	Slightly agree	Agree	Strongly agree
ATTITUDE
Please rate how important are the following activities to you	Options (mark the box with an X)
The recirculation of water from fruit washing is	Not important at all	Slightly important	Important	Very important
Wastewater treatment before discharging it outside the farm is	Not important at all	Slightly important	Important	Very important
My participation in decision-making on water management is	Not important at all	Slightly important	Important	Very important
Learning about water management during the crop cycle is	Not important at all	Slightly important	Important	Very important
Participating in farm activities that improve water management is	Not important at all	Slightly important	Important	Very important
Attending trainings on crop water management is	Not important at all	Slightly important	Important	Very important
Receiving training on banana cultivation, soil properties, and water requirements is	Not important at all	Slightly important	Important	Very important
Monitoring the use and quality of water in the production process is	Not important at all	Slightly important	Important	Very important
Participating in the design and implementation of water management guidelines is	Not important at all	Slightly important	Important	Very important
Please express your level of agreement/disagreement with the following statements	Options (mark the box with an X)
Climate change has consequences on the water management of the farm	Disagree	Slightly agree	Agree	Strongly agree
Agricultural water use has increased during the previous years	Disagree	Slightly agree	Agree	Strongly agree
My actions influence water management on the farm	Disagree	Slightly agree	Agree	Strongly agree
Drinking water must be used when washing the fruit	Disagree	Slightly agree	Agree	Strongly agree
The use of water on banana production may influence consumer health	Disagree	Slightly agree	Agree	Strongly agree
The water demand of the banana crop is high	Disagree	Slightly agree	Agree	Strongly agree
Proper use of water improves long-term profitability	Disagree	Slightly agree	Agree	Strongly agree
PRACTICE
How frequently do you carry out the following actions?	Options (mark the box with an X)
I implement practices related to water reuse and saving on the farm	Never	Occasionally	Very frequently	Always
I attend trainings on water management in agricultural processes and/or banana production	Never	Occasionally	Very frequently	Always
If I notice a water leakage, I report it and/or try to solve it	Never	Occasionally	Very frequently	Always
I wash my boots in the sumps	Never	Occasionally	Very frequently	Always
I throw garbage or other types of waste in the water reservoirs (tanks)	Never	Occasionally	Very frequently	Always
I wash my hands in the water tanks	Never	Occasionally	Very frequently	Always
When I wash my hands, I turn off the tap while soaping my hands	Never	Occasionally	Very frequently	Always

Appendix B. Item scores

Table

Items	X ± SD	Me (R)
Knowledge
The degradation of forests impacts the soil and decreases water availability	3.27 ± 0.83	3.0 (1–4)
I am aware of the sources that supply water to the farm for banana cultivation and washing	3.19 ± 0.75	3.0 (1–4)
Wastewater must be treated to use it for cultivation	2.99 ± 0.94	3.0 (1–4)
The treatment of wastewater after banana washing reduces the pollution of the water bodies that receive the drainage	2.97 ± 0.91	3.0 (1–4)
I am aware of the water treatment system used for fruit washing in the packing house	3.09 ± 0.89	3.0 (1–4)
The recirculation of water in the packing house helps reduce water demand	3.14 ± 0.84	3.0 (1–4)
Water sources can be contaminated by the excessive use of agrochemicals	3.26 ± 0.85	3.0 (1–4)
I am aware of the stream or water body that receives the drainage from the crops	3.01 ± 0.93	3.0 (1–4)
I am aware of the drainage systems of the farm	3.15 ± 0.85	3.0 (1–4)
Attitude
The recirculation of water from fruit washing is	3.46 ± 0.62	4.0 (1–4)
Wastewater treatment before discharging it outside the farm is	3.34 ± 0.62	3.0 (1–4)
Learning about water management during the crop cycle is	3.54 ± 0.55	4.0 (1–4)
Participating in farm activities that improve water management is	3.49 ± 0.55	4.0 (1–4)
Attending trainings on crop water management is	3.50 ± 0.56	4.0 (1–4)
Receiving training on banana cultivation, soil properties, and water requirements is	3.54 ± 0.52	4.0 (2–4)
Monitoring the use and quality of water in the production 7process is	3.43 ± 0.57	3.0 (1–4)
Participating in the design and implementation of water management guidelines is	3.37 ± 0.55	3.0 (1–4)
Climate change has consequences on the water management of the farm	3.13 ± 0.80	3.0 (1–4)
Agricultural water use has increased during the last years	2.91 ± 0.95	3.0 (1–4)
My actions influence water management on the farm	3.16 ± 0.70	3.0 (1–4)
The use of water on banana production may influence consumer health	3.14 ± 0.89	3.0 (1–4)
The water demand of the banana crop is high	3.12 ± 0.85	3.0 (1–4)
Proper use of water improves long-term profitability	3.31 ± 0.69	3.0 (1–4)
Practice
I implement practices related to water reuse and saving on the farm	3.31 ± 0.85	4.0 (1–4)
If I notice a water leakage, I report it and/or try to solve it	3.77 ± 0.54	4.0 (1–4)
I wash my boots in the sumps	2.76 ± 1.37	4.0 (1–4)
I throw garbage or other types of waste in the water reservoirs (tanks)	3.77 ± 0.74	4.0 (1–4)
I wash my hands in the water tanks	3.78 ± 0.73	4.0 (1–4)

X, mean; SD, standard deviation; Me, median; Rn, rank.