Socioeconomic and Cultural Impacts of Native Cotton Cultivation in the Amazonian Communities of Alto Urubamba, La Convencion-Cusco Province, Peru

Abstract

In the native Amazonian communities, there is a significant biodiversity of cotton varieties, where traditional agricultural practices are preserved, crucial for cultural identity and local economic livelihood. This study examines the socioeconomic and cultural impacts of native cotton cultivation in the Amazonian communities of Alto Urubamba, La Convención-Cusco, Peru. Through a structured survey encompassing eight dimensions—general data, family and household, housing and services, education, economy and work, perspectives and challenges, and community participation—data was collected from residents of the Koribeni, Poyentimari, and Chacopishiato communities, particularly artisans involved in cotton-related activities. The analysis revealed agriculture as the primary income source, with 94.1% of Chacopishiato, 100% of Koribeni, and 61.1% of Poyentimari respondents depending on it. Participation in native cotton activities varied, with 52.9% in Chacopishiato, 88.2% in Koribeni, and 33.3% in Poyentimari. Common challenges include limited access to quality seeds (68.8% in Chacopishiato) and a need for technical knowledge (100% in Koribeni and Poyentimari). Correlation analysis showed significant associations between the study’s dimensions. Variations in community perceptions and knowledge about cotton cultivation suggest the need for targeted interventions. This research underscores the importance of sustainable development strategies that integrate traditional agricultural practices, preserve biodiversity, and enhance community resilience in the region.

1. Introduction

Globally, cotton (Gossypium ssp.) represents a crucial economic pillar, especially for developing countries. This crop is a vital source of income for millions of small farmers, and its production and international trade are indicative of its strategic importance in the global market [1]. The sector not only drives the economy but is also a large employer, integrating 150 million people into its value chain in 75 countries, which positions it as a key element for the sustainable development goals of the 2030 Agenda [2].

At the global level, India is the largest cotton producer in the world, with a production of 6,131,050 metric tons per year, followed by the People’s Republic of China and the United States of America with 5,910,500 metric tons and 3,180,410 metric tons of production per year, respectively. Peru is ranked 46th with a production of 6200 metric tons [2].

Cotton production is fundamental to the economies of at least 75 producing countries around the world. In the Latin American and Caribbean regions, cotton is important for rural territories and economies, with 80% produced mainly by family farmers. In the case of Peru, the cotton value chain generates jobs for 430,000 people, of which more than 10,000 are family farmers [3].

Cotton cultivation in Peru is mainly based in Amazonian communities. Studies carried out by Morales et al. [4,5] demonstrated the great diversity of cotton that is present in the districts of Echarati in the province of La Convencion, with a very important climate that helps and improves the growth of cotton for those native communities that preserve and have cotton as their only resource. The native communities of Chacopishiato, Koribeni, and Poyentimari are located in the Cusco region, Peru, in the heart of the lush Amazon. These communities, belonging to the Matsigenka people, have maintained a close relationship with the land and its natural resources for centuries, developing a rich and diverse agricultural culture [6,7]. The ancient culture preserved in these communities maintains the associated traditional knowledge through generations and, with this, maintains cultural identity, consumption habits, and agronomic practices, among others [7].

Among the activities that these communities practice is the making of clothing from the cotton that they maintain and grow themselves. The cultivation of native cotton, known locally as “cumush”, has great cultural and economic importance for the communities of Chacopishiato, Koribeni, and Poyentimari [4,5,8,9]. Cotton is used to weave traditional clothing, hammocks, bags, and other utilitarian items. Cumush fiber is also used to make handicrafts, which are sold in local and regional markets [4].

The maintenance of these practices in native communities contributes to the cultural heritage of the region, and within this region, it has been shown that cotton cultivation is important and shows a wide diversity of species [10,11,12]. This diversity preserved in these communities when accessed for research purposes shows their potential, as has recently been shown by Morales-Aranibar et al. [4]. These authors demonstrated that genetic resources are conserved in these Peruvian Amazon communities and that these resources influence people’s lives because they are part of the family livelihood.

The communities of Chacopishiato, Koribeni and Poyentimari are valuable for understanding the relationships among indigenous communities, agriculture and the cultivation of native cotton in the Peruvian Amazon [13]. The objective of this study is to evaluate, through a survey, the effects of the production and maintenance of native cotton on socioeconomic and cultural impacts in the Amazonian communities of Alto Urubamba, province of La Convencion-Cusco.

2. Methodology

2.1. Theoretical Background

The study proposed by Pisani, Masiero, and Scrocco [14] examines the economic feasibility of reintroducing native cotton cultivation to the northern coast of Peru, emphasizing its cultural significance and potential benefits for small farmers. The study employs farm economic data analysis, scenario analysis, and sensitivity analysis to evaluate the impact on farm incomes under various production scenarios. The findings indicate that reintroducing native cotton can increase average farm incomes, even under adverse conditions, such as a 10% price decrease. This reintroduction not only supports the preservation of cultural heritage but also offers a viable economic opportunity for rural communities, enhancing their resilience and sustainability.

In another study, De Hoop et al. [15] assess the social and economic impacts of cotton farming in Madhya Pradesh, India, focusing on organic cotton farmers, those licensed by the Better Cotton Initiative (BCI), and conventional ones. The BCI is a global organization that promotes more sustainable standards in cotton production. Cotton cultivation is crucial for rural communities, providing significant employment opportunities and contributing to economic stability. The study reveals that organic and BCI-licensed farmers are generally better off socio-economically than conventional farmers, though they face challenges, such as higher indebtedness and lower yields. The findings underscore the need for improved support and incentives to enhance the adoption and sustainability of organic and BCI cotton farming practices.

Rocha-Munive et al. [16] evaluate the impact of genetically modified (GM) cotton cultivation over 20 years in Mexico, highlighting its significance for rural communities reliant on cotton for fiber, oil, and livestock feed. GM cotton, particularly Bt cotton, has been instrumental in reactivating cotton production by effectively controlling lepidopteran pests, reducing the need for chemical insecticides, and mitigating environmental and health risks. The study underscores the economic benefits for farmers, despite the high costs of GM seeds and herbicides, and notes the successful management of herbicide resistance. However, it emphasizes the need for continued monitoring to prevent gene flow to wild relatives and to address emerging pest issues. The article concludes that GM cotton has provided substantial agronomic and environmental benefits, but ongoing research and adaptive management are crucial for sustainable cultivation.

Altenbuchner, Larcher, and Vogel [17] examine the impact of organic cotton cultivation on the livelihoods of smallholder farmers in the Meatu district of Tanzania. Their results show that cotton is a crucial income source for these farmers, but conventional methods have significant negative effects due to high pesticide use and intensive land practices. The study identifies key motivations for farmers to switch to organic farming, including access to knowledge and training and the organic price premium. Despite challenges during the conversion period, organic farming has led to improved soil fertility, increased yields, and higher incomes, contributing to better living standards and enhanced resilience against environmental and economic challenges. However, issues such as gender disparities, poor education and health care systems, and environmental factors, such as water scarcity, remain significant obstacles. Overall, organic cotton farming has positively influenced the livelihoods of farmers, though substantial challenges persist.

Finally, a study conducted by Radhakrishnan [18] on sustainable cotton production emphasizes the critical role of cotton as a leading cash crop, engaging over 350 million people globally and significantly contributing to the economies of many countries. It highlights the shift from traditional, unsustainable cultivation methods to more sustainable practices aimed at protecting farmers’ livelihoods and the environment. The research underscores the importance of regulatory measures and production standards in promoting sustainable farming. It concludes that integrating sustainable practices in cotton farming not only enhances environmental health but also supports economic profitability and social equity, ultimately benefiting rural communities and ensuring the long-term viability of cotton production.

The collection of studies reviewed underscores the multifaceted role of cotton cultivation in promoting economic sustainability, social equity, and environmental stewardship across different global contexts. Each study highlights unique regional challenges and benefits associated with various cotton farming practices, from the reintroduction of native cotton in Peru to the adoption of organic and GM cotton in India, Mexico, and Tanzania. Collectively, these findings emphasize the importance of tailored support mechanisms, regulatory frameworks, and ongoing research to optimize cotton farming’s contributions to rural livelihoods while also preserving cultural heritage and mitigating environmental impacts. This body of work provides a critical foundation for understanding the complex interplay between cotton cultivation practices and sustainable development, informing future efforts to enhance the resilience and sustainability of cotton-producing communities worldwide.

2.2. Study Design

The design of this study is descriptive–analytical and uses surveys to explore the relationships between specific variables. The independent variables include the educational level, experience in cotton cultivation, and community involvement, while the dependent variables are knowledge about the use of native cotton and the perceived sustainability of agricultural practices. The study’s hypotheses are that there is a significant relationship between the respondents’ educational level and their knowledge about the use of native cotton and that experience in cotton cultivation influences the perceived sustainability of agricultural practices.

2.3. Study Area

The study was conducted in the jungle of La Convención Province in the Cusco Region. This area was selected for its cultural, economic, and ecological importance in native cotton cultivation, as well as for its representativeness in the diversity of agricultural practices among Amazonian communities. The study took place between October 2022 and December 2023, in the native Amazonian communities of Chacopishiato between 1086 and 1137 m, Poyentimari between 497 and 506 m, Koribeni between 677 and 746 m, and in the Echarati district located between the coordinates of the UTM WGS-84 Zone: 18 South (763082 E and 8587340 N). Permission was requested from the native Amazonian communities for collection.

2.4. Population and Samples

The sample was selected using non-probabilistic convenience sampling, focusing on individuals directly involved in cotton agriculture in the selected native communities. The research universe encompasses all farmers from the communities of Koribeni, Poyentimari, and Chacopishiato, with artisans who safeguard the germplasm being the main sources of production and conservation of native cotton. The sample included a wide range of individuals of different ages, genders, and levels of experience in cotton cultivation. On average, 18 people were interviewed in each community, totaling 54 people across the three communities, providing a significant representation of the diverse practices and knowledge within these communities. Every effort was made to interview all individuals who cultivate cotton.

2.5. Design and Structure of the Survey

A non-probabilistic sample was used for convenience, whereby the selection of the sample was carried out according to the criteria of the researcher [12,19,20]. The survey is structured into eight main dimensions:

(1)

General Data: This includes questions 1 to 9.

(2)

Family and Household: This comprises questions 10 to 12.

(3)

Housing and Basic Services: This contains questions 13 to 17.

(4)

Education: This scale encompasses questions 18 to 22.

(5)

Economy and Work: This covers questions 23 to 33.

(6)

Perspectives and Challenges: This consists of questions 34 to 36.

(7)

Community Participation: This includes questions 37 and 38.

(8)

Observations and Suggestions: These are gathered in question 39.

Additionally, informed consent was obtained from each respondent, ensuring the ethics and legality of the data collection process. In total, 39 questions were formulated (Appendix A).

The information collection methodology was structured into three phases:

(a)

Application of the survey: The survey was carried out to select individuals linked to or involved in the cultivation of cotton within the three selected communities.

(b)

Data Review: Critical evaluation of the socioeconomic survey, including the validation of the methodology used, the representation of the sample, and the reliability of the data.

2.6. Survey Validation and Reliability

The survey instrument was developed by the research team and underwent a validation process by experts in the cotton study to improve its initial pilot version. This initial survey was administered in the field, and based on the feedback—based on the ideologies and experiences of the respondents—it was necessary to refine the instrument with real data obtained. This iterative process was validated using Cronbach’s alpha, a measure of internal consistency. Initially, the alpha values did not reach acceptable thresholds. However, after revising the survey based on expert feedback, the revised instrument reached Cronbach’s alpha values of 0.76 for Koribeni, 0.91 for Poyentimari, and 0.82 for Chacopishiato. These values indicate statistically acceptable levels of reliability for the data collected in the different communities.

2.7. Data Collection Process

The data were collected using questionnaires distributed in the communities mentioned above. These questionnaires were designed to be completed individually, ensuring an accurate representation of the socioeconomic and agricultural conditions of Peruvian Amazon communities. Crafts and textile farmers who work with cotton were selected, cultivated, and preserved in small plots in different zones.

2.8. Statistical Analysis

Descriptive techniques, analysis using χ² tests, contingency coefficient, Cramer’s V, and correlation analysis were used to examine the relationships between the presence of cotton cultivation and the socioeconomic factors identified in the survey. The information of the answers to each question was transformed into numerical data. We then assigned to each answer a consecutive value of 1 to n answers, and, with these values, a Pearson correlation was established. Using the correlation matrix obtained, a correlation network was constructed, from which the red and green traces correspond to negative and positive correlations, respectively. All correlations greater than 0.6 are highlighted. These analyses were performed using Rbio software version 166 for Windows (Rbio Software, UFV, Viçosa, MG, Brazil).

3. Results

In Peruvian Amazon communities, a process of conservation and use of cotton genetic resources has been established. Evaluating the causes that motivate and maintain this conservation at this scale is the main topic of our research. This activity focused on a survey composed of 39 questions that were asked to campaign people living within the Amazonian communities of Chacopishiato, Koribeni, and Poyentimari, located in the District of Echarati, Peru. After the surveys were carried out in the three selected communities, the data were processed individually and are presented below.

3.1. Chacopishiato

In the survey conducted in the communities of Chacopishiato, Koribeni, and Poyentimari, specific questions received limited responses. For instance, Question_16, regarding basic sanitation, had six non-responses, and Question_18, regarding maternal language, received one non-response. Additionally, Question_34, which asked about the main challenges in native cotton production, also saw limited feedback. The eight non-responses across all surveyed questions highlight areas where respondents may require additional support or where adjustments in survey methodology might be necessary to ensure more comprehensive data collection.

The only question with a real numerical value is Question_1. Here, the average age was 43.6 ± 9.86 years. For the other questions, the frequencies of responses were computed as shown in

Table 1. Frequencies of interviewees’ answers to each of the questions in Chacopishiato, Koribeni, and Poyentimari, located in the District of Echarati, Peru.

Question	Options	(A) Chacopishiato		(B) Koribeni		(C) Poyentimari
		Counts	Total (%)	Counts	Total (%)	Counts	Total (%)
Question_2	○ Female	14	82.4	14	82.4	16	88.9
	○ Male	3	17.6	3	17.6	2	11.1
Question_3	○ Cohabitant	5	29.4	0	0	11	61.1
	○ Married	6	35.3	12	70.6	1	5.6
	○ Single	6	35.3	5	29.4	6	33.3
Question_6	○ No	4	23.5	4	23.5	4	22.2
	○ Yes	13	76.5	13	76.5	14	77.8
Question_13	○ Assigned/Loaned without payment	2	11.8	1	5.9	1	5.9
	○ Own	15	88.2	14	82.4	17	94.4
	○ Rented	0	0	2	11.8	1	5.6
Question_14	○ No	11	64.7	4	23.5	14	77.8
	○ Yes	6	35.3	13	76.5	4	22.2
Question_15	○ Does not have	0	0	0	0	1	5.6
	○ Electric	14	82.4	13	76.5	13	72.2
	○ Generator	0	0	1	5.9	0	0
	○ Solar panel	3	17.6	3	17.6	4	22.2
Question_16	○ No	6	54.5	5	29.4	2	11.1
	○ Yes	5	45.5	12	70.6	16	88.9
Question_17	○ Drinking water	1	5.9	2	11.8	1	5.6
	○ Electric power	15	88.2	15	88.2	15	83.3
	○ Natural gas	0	0	0	0	1	5.6
	○ None of the above	1	5.9	0	0	1	5.6
Question_18	○ Matsigenka	16	100.0	17	100.0	18	100.0
Question_19	○ No	0	0	0	0	2	11.1
	○ Yes	17	100.0	17	100.0	16	88.9
Question_20	○ Complete primary education	4	23.5	0	0	5	27.8
	○ Complete secondary education	1	5.9	9	52.9	3	16.7
	○ Complete technical or vocational education	0	0	4	5.9	1	5.6
	○ Incomplete primary education	9	52.9	6	35.3	2	11.1
	○ Incomplete secondary education	0	0	0	0	5	27.8
	○ Incomplete university education	0	0	0	0	1	5.6
	○ Incomplete technical or professional education	0	0	1	5.9	0	0
	○ None	3	17.6	0	0	1	5.6
Question_21	○ Yes	17	100.0	17	100.0	18	100.0
Question_23	○ Agriculture (native cotton, other crops)	16	94.1	17	100.0	11	61.1
	○ Cattle raising	1	5.9	0	0	1	5.6
	○ Commerce	0	0	0	0	1	5.6
	○ Independent	0	0	0	0	5	27.8
Question_24	○ 1600–2000	0	0	0	0	1	5.6
	○ 500–1500	4	23.5	14	82.4	9	50.0
	○ Less than 500	13	76.5	3	17.6	8	44.4
Question_25	○ No	14	82.4	14	82.4	15	83.3
	○ Yes	3	17.6	3	17.6	3	16.7
Question_26	○ No	6	35.3	1	5.9	13	72.2
	○ Yes	11	64.7	16	94.1	5	27.8
Question_27	○ It is unknown	2	11.8	0	0	0	0
	○ No	7	41.2	0	0	4	22.2
	○ Yes	8	47.1	17	100.0	14	77.8
Question_28	○ For clothing	10	58.8	13	76.5	12	66.7
	○ Handicrafts	7	41.2	2	11.8	3	16.7
	○ Medicine	0	0	2	11.8	3	16.7
Question_29	○ Close to streams and roads	3	17.6	0	0	5	27.8
	○ High mountain	0	0	1	5.9	1	5.6
	○ I don’t see anywhere	1	5.9	1	5.9	2	11.1
	○ Yes, around my chakras	13	76.5	15	88.2	10	55.6
Question_30	○ Own Production (1)	10	58.8	16	94.1	12	66.7
	○ Sylvester Collection (2)	6	35.3	1	5.9	5	27.8
	○ 1 and 2	1	5.9	0	0	1	5.6
Question_31	○ Own Production	15	88.2	17	100.0	14	77.8
	○ Own Production e Sylvester	2	11.8	0	0	2	11.1
	○ Sylvester	0	0	0	0	2	11.1
Question_32	○ Lack of access to quality seeds	2	11.8	17	100.0	3	16.7
	○ Limited technical knowledge	2	11.8	0	0	6	33.3
	○ Others	1	5.9	0	0	0	0
	○ Pests and diseases	12	70.6	0	0	8	44.4
	○ Unfavorable weather conditions	0	0	0	0	1	5.6
Question_33	○ Do not know	0	0	0	0	2	11.1
	○ No	0	0	1	5.9	9	50.0
	○ Yes	17	100.0	16	94.1	7	38.9
Question_34	○ Access to agricultural technology and tools	3	18.8	0	0	3	16.7
	○ Access to markets and commercialization	1	6.3	0	0	1	5.6
	○ Access to quality seeds	11	68.8	17	100.0	11	61.1
	○ Pests and diseases problems in the crop	1	6.3	0	0	0	0
	○ Lack of government support	0	0	0	0	2	11.1
	○ Limitations in irrigation infrastructure	0	0	0	0	1	5.6
Question_35	○ Access to financing and agricultural credits	5	29.4	0	0	3	16.7
	○ Improvement in irrigation infrastructure	0	0	0	0	1	5.6
	○ Improvement of the marketing chain and access to markets	0	0	0	0	1	5.6
	○ Promoting crop diversification	1	5.9	0	0	0	0
	○ Technical training in native cotton cultivation and management techniques	11	64.7	17	100.0	13	72.2
Question_36	○ No, I do not use agricultural technology	17	100.0	17	100.0	15	83.3
	○ Yes, to a large extent	0	0	0	0	1	5.6
	○ Yes, to some extent	0	0	0	0	2	11.1
Question_37	○ No	8	47.1	2	11.8	12	66.7
	○ Yes	9	52.9	15	88.2	6	33.3

(A) for both absolute quantities and percentage values.

The relationship between educational background and cotton cultivation experience was investigated. It compares the responses to two survey questions: Question_6 regarding experience with cotton cultivation in Echarati and Question_20 on the respondent’s level of education (Table S1). The analysis using χ² tests yielded some findings but not necessarily a strong connection. The contingency coefficient (0.258) and Cramer’s V (0.267) are relatively low, suggesting a weak or modest association between education level and cotton cultivation experience. This is further supported by the high p values obtained from both the Fisher’s exact test (1.000) and the likelihood ratio test (0.674). A high p value indicates that any observed association between education and experience could be due to chance, and there is no statistically significant relationship between the two variables.

This sheds light on the connection between experience in the Echarati district (Question_7) and educational background (Question_20) (Table S2). The χ² tests revealed a strong association between the two questions. Cramer’s V, with a value of 0.733, indicates a moderate-to-strong positive relationship. This means that people with experience in Echarati are more likely to have a certain level of education, or vice versa. Further solidifying this connection are the p values from both the Fisher’s exact test (0.018) and the likelihood ratio test (0.017). These very low p values imply that the observed association is statistically significant and unlikely to be due to chance.

The relationship between past experience with cotton cultivation in Echarati (Question_6) and current knowledge about its benefits (Question_23) (Table S3). The χ² test results suggest a weak or nonexistent association between the two variables. The likelihood ratio has a value of 0.259 with a high p value of 0.611. This indicates that any observed association is likely due to chance. Similarly, the contingency coefficient (0.0909) and Cramer’s V (0.0913) are very close to zero, further supporting the notion of a weak connection.

The link between past cotton cultivation experience in Echarati (Question_7) and current knowledge regarding its benefits (Question_23) (Table S4). The χ² test results reveal a weak or absent connection between the questions. The likelihood ratio’s value of 0.555 with a high p value of 0.456 suggested that any observed association was likely due to chance. This is further supported by the contingency coefficient (0.137) and Cramer’s V (0.139). Fisher’s exact test confirmed this conclusion, with a p value of 1.000. This high p value signifies no statistically significant relationship between past experience cultivating cotton in Echarati and current knowledge about its advantages.

There is a possible connection between past cotton cultivation experience in Echarati (Question_6) and the interviewee’s monthly income (Question_24) (Table S5). The χ² test results indicate a weak or nonexistent association. The p values from both the Fisher’s exact test (0.426) and the likelihood ratio test (0.382) are relatively high. This is further reinforced by the contingency coefficient (0.222) and Cramer’s V (0.228), which are both low values. These coefficients indicate a weak connection at best.

The link between past cotton cultivation experience in the Echarati district (Question 7) and the interviewee’s monthly income (Question 24) (Table S6). The χ² test results overwhelmingly suggest that there is no significant connection between the two variables. This is evident from the p values obtained. Both the Fisher’s exact test (1.000) and the likelihood ratio test (0.937) have very high p values. Furthermore, the contingency coefficient and Cramer’s V are both extremely low at 0.0192, which signifies an almost nonexistent connection.

The connection between past cotton cultivation experience in Echarati (Question_6) and the belief in a market for its revival (Question_27) (Table S7). The χ² test results hint at a moderate association. The contingency coefficient (0.400) and Cramer’s V (0.436) suggested a moderate positive relationship. This means that people with experience cultivating cotton might be more likely to believe there is a demand for it, or vice versa. The p value of the Fisher’s exact test (0.382) was not statistically significant, but it was not entirely negligible either. Similarly, the p value of 0.139 for the likelihood ratio indicates that the observed associations may not be purely coincidental.

The relationship between past cotton cultivation experience (Question_7) and the belief in demand for its cultivation (Question_27) (Table S8). The χ² test results suggest a moderate connection between the two variables. The contingency coefficient (0.365) and Cramer’s V (0.392) indicate a weak-to-moderate positive association. This means that people with experience cultivating cotton might be more likely to believe there is a demand for it, or vice versa. However, the p values from Fisher’s exact test (0.282) and the likelihood ratio test (0.228) are somewhat high. While they do not completely rule out a connection, they do suggest the possibility that the observed association could be due to chance.

The link between past experience with cotton cultivation (Question_6) and knowledge about the uses of native cotton (Question_28) has been explored through χ² tests (Table S9). The likelihood ratio has a value of 2.31 with a p-value of 0.129, suggesting a possible, though not certain, relationship between these two variables. The p value of the Fisher’s exact test (0.485) supports the conclusion that the results are inconclusive. The strength of this association is reflected in the contingency coefficient (0.292) and Cramer’s V (0.306), indicating a weak to moderate connection. This analysis contrasts with findings from statistical tests applied to other variables discussed in earlier sections of this study, where stronger and statistically significant relationships were observed.

The connection between experience cultivating cotton in Echarati (Question_7) and knowledge about the uses of native cotton (Question_28) (Table S10). The likelihood ratio has a value of 2.31 with a p value of 0.129. This suggests a possibility, but not a certainty, that there is a relationship between the two questions. Similarly, the p value of Fisher’s exact test (0.485) indicated an inconclusive outcome. The contingency coefficient (0.292) and Cramer’s V (0.306) mirror the findings. These values indicate a possible weak to moderate association between experience in Echarati and knowledge about the uses of native cotton.

3.2. Correlation Network Obtained for the Amazonian Community Chacopishiato

Using the correlation matrix obtained from this community, a correlation network was constructed, from which the red and green traces correspond to negative and positive correlations, respectively. Figure 1 shows that there are prominent positive and negative correlations (<0.65) among the questions asked of residents of the Chacopishiato community. High and positive significant correlations were found between Q29 and Q31 (0.76) and between Q32 and Q18 (0.70). The list of questions Q29 (knowledge of wild native cotton plants) and Q31 (status of native cotton in your chakra) shows that whenever the interviewees had knowledge of cotton cultivation and its benefits and importance, they maintained cultivation in your chakras. The combination of Q32 (the main difficulty in growing native cotton) and Q18 (the mother tongue) shows that the Matsigenka mother tongue is the one that predominates in the Chacopishiato community, and that knowledge of this tongue has made it possible to recognize the pests and diseases of the culture, often as associated traditional knowledge passed down from generation to generation. Significant negative correlations were detected when comparing questions Q27 and Q35 (−0.77), Q26 and Q29 (−0.71), and Q26 and Q3 (−0.66) (Figure 1). The relationship between Q27 (do you believe that the cultivation of native cotton is in demand?) The negative correlation of Q35 (measures to improve production) shows that peasants recognize that cultivation has demand and that measures to improve production are necessary.

3.3. Koribeni

Since the only question with a real numerical value is Question_1, we compute the average age, which is 47.2 ± 12.2. For the other questions, both for absolute and percentage values, the frequencies of responses were computed, as shown in Table 1 (B). It is important to highlight that, unlike the responses to the questionnaire relating to the Chacopishiato region, where there were abstentions for some questions (Table 1 (B)), for the Koribeni region, there were no abstentions.

The relationship between the level of education and cotton cultivation. It compares the answers to Question_6 with those of Question_20 (Table S11). The strength of this association is measured by several statistics. The contingency coefficient is 0.507, and Cramer’s V is 0.588, indicating a moderate association, while Fisher’s exact test p value (0.138) and the likelihood ratio p value (0.069) suggest that the results are marginally significant.

The link between educational background and cotton cultivation experience in the Echarati district (questions 7 and 20) (Table S12). The analysis revealed a moderate association between the two questions. The contingency coefficient (0.507) and Cramer’s V (0.588) indicate a moderate strength with this connection. Compared with people with lower education levels, people with higher education levels might be more or less likely to cultivate cotton in Echarati. However, the p values from both the Fisher’s exact test (0.138) and the likelihood ratio test (0.069) are somewhat high. While they suggest a possible connection, they fall short of definitive statistical significance. There is a chance that the observed association could be due to random factors.

Comparisons between the answers to Question_6 and Question_7 with respect to Question_23 were not carried out because for Question_23, there was only an answer for the alternative “Agriculture (native cotton, other crops)”. Question_6 and Question_24 were compared to determine how they relate to the interviewee’s monthly income (Table S13). The χ² tests showed no statistically significant associations. The p value of Fisher’s exact test was 1.000, and the likelihood ratio p value was 0.669. The contingency coefficient (0.106) and Cramer’s V (0.107) also indicate a weak relationship.

The relationship between experience cultivating cotton in Echarati (Question_7) and monthly income (Question_24) (Table S14). The chi-square tests revealed no statistically significant correlation between the two variables. The p values from both the Fisher’s exact test (0.541) and the likelihood ratio test (0.180) are relatively high. A high p value suggests that any observed link between cultivation experience and income is likely due to chance. This is further supported by the contingency coefficient (0.249) and Cramer’s V (0.257), which are both low values. These coefficients indicate a weak connection at best.

Analysis of the answers to Question_6 and Question_7 in relation to Question_27 was not carried out because for Question_27, there was only one answer for the alternative “Yes”.

The relationship between education level (Question_6) and the use of native cotton (Question_28) (Table S15). The χ² tests indicated a statistically significant association. The p value for the likelihood ratio is 0.025, suggesting a strong association at the 5% significance level. However, the p value of Fisher’s exact test (0.088) was marginally nonsignificant. The strength of the association is further confirmed by the contingency coefficient (0.556) and Cramer’s V (0.668), both indicating a moderate to strong relationship.

The relationship between educational level and knowledge about native cotton: Question_7 and Question_28 (Table S16). The χ² tests provide strong evidence of a connection. The likelihood ratio p value of 0.013 and Fisher’s exact test p value of 0.022 are both very low. This means that it is highly unlikely that the observed associations are simply coincidental. Furthermore, the contingency coefficient (0.590) and Cramer’s V (0.731) indicate a moderate-to-strong positive relationship. People with experience cultivating cotton in Echarati (Question_7) are more likely to have knowledge about the various uses of native cotton (Question_28) than are those without such experience.

3.4. Correlation Network Obtained for the Amazonian Community of Koribeni

Using the correlation matrix obtained from this community, a correlation network was constructed, from which the red and green traces correspond to negative and positive correlations, respectively. When observing the correlation network obtained to relate the questions in the community of Koribeni, positive and significant correlations were obtained between questions Q14 and Q16 (0.86), between questions Q14 and Q7 (1.0), between questions Q7 and Q16 (0.86), and between questions Q6 and Q16 (0.86) (Figure 2). Significant negative correlations were established between Q14 and Q28 (−0.73), between Q7 and Q28 (−0.73), and between Q30 and Q37 (−0.68). For question Q14 (Does your home have a property title?) and Q16 (Do you have basic sanitation in your home?), a positive relationship is established, showing that the lack of property in the houses of the peasants is associated with the lack of basic sanitation for this community. These two questions (Q14 and Q16) are positively associated with questions Q6 and Q7 (Have you grown native cotton in the Echarati area?) (Figure 2).

3.5. Poyentimari

Similarly, for the questionnaire in the Poyentimari region, all questions were answered for the Koribeni region. The only question with a real numerical value is Question_1, so the average age of the respondents is 37.6 ± 10.1 years. The response frequencies for the other questions were computed, as shown in Table 1 (C).

The relationship between education level (Question_20) and cotton cultivation (Question_6) (Table S17). In other words, it analyzes how the answers to these two survey questions are connected. The strength of this association is measured by several statistical tests. The contingency coefficient (0.588) and Cramer’s V (0.727) indicate a moderate to strong association. However, the p values from Fisher’s exact test (0.176) and the likelihood ratio test (0.115) are both greater than 0.05.

There is a strong connection between educational background (Question_20) and experience cultivating cotton in Echarati (Question_7) (Table S18). The χ² tests yielded statistically significant results. Both the contingency coefficient (0.663) and Cramer’s V (0.886) indicate a strong positive association. This means that people with higher education levels are more likely to cultivate cotton in Echarati than are those with lower education levels. The p values further solidify this conclusion. Both the Fisher’s exact test (0.011) and the likelihood ratio (0.018) have very low p values, signifying a high degree of certainty that the observed link is not due to random chance.

The analysis of the relationship between Question_6 and Question_23 (income source) revealed a statistically significant association (Table S19). This conclusion is based on multiple tests. The χ² test statistic (8.64) with a p value of 0.035 indicates a connection at the 5% significance level. In other words, it is very unlikely (less than 3.5% chance) to see this association by random chance. Furthermore, the strength of the association was assessed by the contingency coefficient (0.567) and Cramer’s V (0.688). These values suggest a moderate to strong association between the two questions. Finally, Fisher’s exact test also reinforced the connection, with a p value of 0.047, providing additional support for the results.

The connection between experience cultivating cotton in Echarati (Question_7) and current income sources (Question_23) (Table S20). The analysis revealed a moderate-to-strong association between the two variables, with some nuance based on the chosen level of statistical significance. The contingency coefficient (0.557) and Cramer’s V (0.671) indicate a positive relationship. This means that people with experience cultivating cotton in Echarati might be more likely to have a certain income source than people without such experience. The key takeaway comes from Fisher’s exact test, which yields a p value of 0.047. Considering a confidence level of 0.05 (95% confidence), this p value allows us to reject the null hypothesis. In simpler terms, the results suggest a statistically significant association between experience and income source. However, the likelihood ratio test presents a slightly different picture. Its p value of 0.061 is higher than the chosen confidence level. While this does not definitively disprove a connection, it also does not provide conclusive evidence on its own. When we consider both tests together, a compelling argument emerges. Fisher’s exact test offers strong statistical support for a moderate-to-strong association. The likelihood ratio, while not reaching the same level of significance, leans toward the possibility of a link.

The χ² test (Fisher’s exact test: p value = 0.286, likelihood ratio: p value = 0.173) does not provide enough evidence to reject the null hypothesis of independence between Question_6 and Question_24 at the 5% significance level. In other words, based on these tests, we cannot say that there is a statistically significant association between the two questions (Table S21). However, the effect sizes, contingency coefficient (0.424), and Cramer’s V (0.468) indicate a moderate association between the two variables. Although the χ² tests did not show a statistically significant relationship, the effect sizes suggest that there might be a connection between the answers to Question_6 and Question_24 that is worth exploring further.

The relationship between experience cultivating cotton in Echarati (Question_7) and monthly income (Question_24) (Table S22). The χ² tests provide some hints of a connection but not a definitive one. The p values from both the Fisher’s exact test (0.286) and the likelihood ratio test (0.173) are relatively high. A high p value suggests that any observed association between cultivation experience and income is likely due to chance. This is further supported by the contingency coefficient (0.424) and Cramer’s V (0.468), which are moderate values. These coefficients indicate a possible weak to moderate connection at best.

The analysis employed a statistical test, which yielded a contingency coefficient and Cramer’s V of 0.0357. These values are very close to zero, indicating a weak or negligible association between the two questions. Further supporting this conclusion, Fisher’s exact test produced a p value of 1.000, signifying that there was no statistically significant relationship between the two questions. Similarly, the likelihood ratio of 0.881 reinforces the absence of a strong connection. In simpler terms, the results suggest that whether native cotton was previously grown in the area has little to no bearing on people’s beliefs about the demand for its cultivation (Table S23).

The link between experience cultivating cotton in Echarati (Question_7) and the belief in a demand for its revival (Question_27) (Table S24). However, the statistical tests paint a picture of little to no connection between the two, analogous to the results since they have the same statistical values.

The link between two survey questions, Question_6 and Question_28, about the perceived use of native cotton (Table S25). The analysis revealed several interesting connections. The likelihood ratio yielded a p value of 0.109, which suggests the possibility (but not certainty) that a relationship exists between the two questions. The value of the likelihood ratio itself is 4.44. Further insights come from Fisher’s exact test, which produced a p value of 0.213. Again, this value leans toward a potential association, although not a definitive one. The strength of this association is reflected in the contingency coefficient (0.447) and Cramer’s V (0.500). These values are considerably greater than those observed in previous analyses, indicating a moderate-to-strong connection between whether native cotton was previously grown and people’s understanding of its uses. In other words, there seems to be a connection between past cultivation and the present understanding of the uses of native cotton. People who live in areas with a history of native cotton cultivation might have a better understanding of its purpose than people who do not.

The connection between experience cultivating cotton in Echarati (Question_7) and knowledge about native cotton uses (Question_28) (Table S26). The statistical tests yielded are mirrored in the analysis; therefore, the conclusions about the association between the questions analyzed here are the same.

3.6. Correlation Network Obtained for the Amazonian Community Poyentimari

Using the correlation matrix obtained from this community, a correlation network was constructed, from which the red and green traces correspond to negative and positive correlations, respectively. When observing the correlation network obtained to relate the questions carried out in the community of Poyentimari, it was observed that positive and significant correlations were obtained between questions Q1 and Q25 (0.71), between Q6 and Q7 (0.68), between Q7 and Q19 (0.66), between Q34 and Q35 (0.65), between Q35 and Q28 (0.63), and between Q30 and Q31 (0.66). Significant negative correlations were established between Q31 and Q27 (−0.73), between Q23 and Q19 (−0.61), and between Q23 and Q7 (−0.61) (Figure 3).

4. Discussion

To determine how experience with the cultivation or noncultivation of native cotton impacts family, education, and economic issues in the areas of Chacopishiato, Koribeni, and Poyentimari, we separately discuss the results obtained from the questionnaire. This region has been shown to have high potential for conserving cotton genetic resources [4], which is closely associated with the maintenance of cotton species of the genera Gossypium hirsutun L. [4], Gossypium barbadense L. [4,5], and Gossypium barbadense L. var. brasiliensis [9]. The diversity found in these previous works serves as the basis for the present work and lays the foundation for understanding the socioeconomic and cultural impact of native cotton in the Amazonian communities of Alto Urubamba, province of La Convencion-Cusco, Peru.

Family farming is the basis of the economy in these communities. The main crops include cassava, corn, bananas, beans, and peanuts [10]. These products are mainly intended for self-consumption and commercial exchange with neighboring communities [21,22]. Small-scale livestock farming, fishing, and the extraction of nontimber forest products also contribute to the local economy. The three communities share similarities in their agricultural practices and the importance of native cotton [4,12]. However, there are also some differences. For example, Chacopishiato is characterized by greater cassava production, while Koribeni stands out for its corn production. Poyentimari, for its part, has greater participation in the extraction of nontimber forest products [12].

4.1. Chacopishiato

The statistical analysis (Table 1) provides overarching insight into the socioeconomic landscape and challenges encountered within the native communities of Chacopishiato, particularly concerning the cultivation of native cotton. Predominantly, agriculture emerges as the primary economic activity, with a substantial emphasis on native cotton cultivation alongside other crops, underscoring the pivotal role of the agricultural sector in local livelihoods [10,23]. However, intertwined with this reliance on agriculture, multifaceted challenges warrant further exploration and targeted interventions for sustainable development.

The distribution of respondents based on gender revealed that a significant majority were male (82.4%). Exploring further into familial structures, a majority of respondents were reported to be single (35.3%) or married (35.3%), highlighting the diverse household compositions prevalent within these communities. In terms of housing and basic amenities, a notable majority of residences are self-owned (88.2%), with a significant proportion having access to electricity (82.4%). However, disparities emerge, with a considerable percentage lacking access to basic sanitation (45.5%) or essential services such as potable water, electricity, or sewage facilities (5.9%).

Educationally, the majority of respondents had completed primary education (52.9%), yet a notable proportion lacked formal education (17.6%). Furthermore, the predominant Matsigenka language usage (100%) signifies a profound cultural presence within the studied communities. Economically, agriculture, inclusive of native cotton cultivation and other crops, has emerged as the primary income source (94.1%), accentuating the agricultural sector’s significance. However, a significant proportion reported limited access to credit (82.4%), signifying potential challenges in capital access for agricultural investments.

Regarding perspectives and challenges in native cotton production, a substantial majority of respondents reported experiencing economic benefits (100%). Nonetheless, prevailing challenges such as limited access to quality seeds (68.8%) and pest and disease issues (6.3%) underscore critical barriers necessitating targeted interventions for sustainable production.

Community participation in native cotton-related activities remains relatively high (52.9%), indicating robust social cohesion and collaboration within these communities. The proposed measures for enhancing native cotton production, including improved access to financing and technical training, underscore the need for institutional support and capacity-building initiatives to bolster productivity and resilience amidst economic and environmental challenges [12,24].

The analysis of survey responses from the Chacopishiato community provides insights into the relationships among cotton cultivation experience, educational background, income levels, beliefs about market demand, and knowledge about the uses of native cotton [8,25]. Starting with the connection between educational background and cotton cultivation experience in the Echarati area, the findings indicate a weak or modest association. Despite some variation in educational backgrounds among respondents, the statistical tests revealed relatively low contingency coefficients and Cramer’s V values, suggesting a weak link between education level and cotton cultivation experience. This weak association is further supported by the high p values obtained from both the Fisher’s exact test and the likelihood ratio test, indicating that any observed relationship could occur by chance. Maintaining conservation practices in native communities contributes to maintaining the cultural heritage of the region, and within this region, the results obtained show that cotton cultivation is important from a social and economic point of view [10,11,12] and is associated with a great diversity of cotton species in the region [4].

In contrast, the analysis of the relationship between experience in the Echarati district and educational background reveals a significant and positive association. The strong association indicated by Cramer’s V suggests that individuals with experience in Echarati are more likely to have attained a certain level of education, or vice versa. The low p values obtained from both the Fisher’s exact test and the likelihood ratio test reinforce the statistical significance of this association.

Investigated the connection between Question_6 and Question_23. The statistical tests revealed a weak or nonexistent association between the two variables. Both the likelihood ratio test and Fisher’s exact test yielded high p values, indicating that any observed association was likely due to chance. The relationships between past cotton cultivation experience in the Echarati district (Question_7) and Question_23 were examined, and similar findings emerged. The statistical tests indicate a weak or absent connection between the two variables. Both the likelihood ratio test and Fisher’s exact test produced high p values, suggesting that any observed associations may be coincidental. Thus, it appears that past experience with cotton cultivation does not strongly correlate with current knowledge about its benefits.

Moving on to the relationship between cotton cultivation experience and monthly income, the findings suggest weak or nonexistent relationships. The high p values obtained from the statistical tests and the low contingency coefficients indicate that there is no significant relationship between past cotton cultivation experience and monthly income.

Exploring the belief in market demand for cotton cultivation (Question_27) and its relationship with past cultivation experience revealed moderate associations. Although the p values are not statistically significant, the contingency coefficients and Cramer’s V values suggest a weak to moderate positive relationship between experience with cotton cultivation and belief in market demand.

Finally, examining the link between past cotton cultivation experience and knowledge about its uses (Question_28) revealed moderate associations. While the statistical tests did not yield significant p values, the contingency coefficients and Cramer’s V values suggest a possible weak to moderate connection between past cultivation experience and knowledge about the uses of native cotton.

The survey results highlight varying degrees of association between cotton cultivation experience, educational background, income levels, beliefs about market demand, and knowledge about the uses of native cotton within the Chacopishiato community. These findings underscore the complex interplay of socioeconomic factors and agricultural practices in shaping community perceptions and behaviors related to cotton cultivation. Further research and targeted interventions may be warranted to better understand and address the underlying drivers of these relationships, ultimately supporting sustainable agricultural development and livelihood enhancement initiatives within the community.

4.2. Koribeni

The analysis of survey responses from the Koribeni region provides valuable insights into the socioeconomic characteristics and challenges encountered by native communities regarding the cultivation of native cotton [4]. First, the majority of respondents identified as male (82.4%), indicating potential gender dynamics within these communities that merit further investigation. Moreover, a significant portion of respondents reported being married (70.6%), underscoring the importance of familial structures within these communities. In terms of housing and basic amenities, a notable proportion of respondents reported owning their homes (82.4%), with the majority having access to electricity (88.2%). However, disparities exist, with a considerable percentage lacking access to basic sanitation (29.4%). These findings highlight the need for targeted interventions to address disparities in access to essential services in the community. This information obtained through the questions allows for directing activities in the communities. The information generated is valuable for understanding the relationships among indigenous communities, agriculture, and native cotton cultivation in the Peruvian Amazon [13,26].

Educationally, the majority of respondents had completed at least primary education (52.9%), with a significant portion having completed secondary education (35.3%). However, access to formal education remains a challenge for some members of the community, as evidenced by the proportion reporting incomplete primary education (35.3%). Economically, agriculture emerges as the primary source of income for the majority of respondents (100%), emphasizing the importance of the agricultural sector in the local economy. However, access to credit for agricultural activities remains limited, with the majority of respondents reporting no access to credit (82.4%). These questions allow us to verify that access to information is extremely important in the development of communities, and bodies such as the FAO define educational access as one of the strategies that leads to development, and native communities are not exempt from this [2,27].

Regarding perspectives and challenges in native cotton production, the majority of respondents experienced economic benefits from native cotton cultivation (94.1%). However, challenges such as a lack of access to quality seeds (100%) and limited technical knowledge (100%) pose significant barriers to sustainable production. Seed conservation and even multiplication are conservation strategies that have been reported in other works, such as strategies for direct multiplication of conserved genotypes [10]. Community participation in native cotton-related activities is relatively high (88.2%), indicating a strong sense of community cohesion and collaboration. Moreover, the majority of respondents expressed a desire for technical training in native cotton cultivation and management techniques (100%), highlighting the importance of capacity-building initiatives in enhancing productivity and resilience within the community. The increase in information contributed to the improvement of seed maintenance processes and techniques and thus improved conservation in the reality of native communities [10,21,28].

The associations between past experience with native cotton cultivation (Question_6 and Question_7) and respondents’ knowledge about the various uses of native cotton (Question_20) were examined. The results indicate a moderate association between past cotton cultivation experience and knowledge about native cotton uses. Specifically, individuals who have experience cultivating cotton are more likely to be aware of the various uses of native cotton than are those without such experience [29]. However, the statistical significance of this association is marginal, suggesting that other factors may also influence individuals’ knowledge about native cotton use.

The relationships between past experience with native cotton cultivation in the Echarati area (Question_7) and respondents’ knowledge about native cotton use (Question_20) were explored, and similar patterns emerged. There is a moderate association between past cotton cultivation experience in Echarati and knowledge about native cotton uses, with individuals who have experience cultivating cotton being more likely to be aware of its various uses. Again, the statistical significance of this association is marginal, indicating that other factors may also contribute to individuals’ knowledge about native cotton uses.

The relationship between past cotton cultivation experience (Question_6) and respondents’ monthly income (Question_24) was investigated. Interestingly, no statistically significant associations were found between the two variables. This finding implies that income level may not be a determining factor in whether individuals have experience with cotton cultivation. Similarly, we examined the link between past cotton cultivation experience in Echarati (Question_7) and respondents’ monthly income (Question_24). Again, no significant association is found between the two variables, indicating that income level may not strongly influence individuals’ engagement with cotton cultivation in this district either.

In the contingency table, which analyzes the relationship between past experience with native cotton cultivation (Question_6 and Question_7) and the main use of native cotton (Question_28), a significant association is observed. Individuals with experience cultivating cotton are more likely to be aware of the various uses of native cotton, such as for clothing, handicrafts, or medicine. This suggests that hands-on experience with cotton cultivation may play a crucial role in shaping individuals’ understanding of its diverse applications.

We explored the association between past experience with native cotton cultivation in Echarati (Question_7) and knowledge about the main use of native cotton (Question_28), specifically in that region. The results reveal a strong connection between the two variables, indicating that individuals with experience cultivating cotton in Echarati are more likely to have knowledge about its various uses. This underscores the importance of hands-on experience in shaping individuals’ understanding of agricultural practices and products.

4.3. Poyentimari

The survey conducted in the native communities of the Poyentimari region provides many insights into the socioeconomic characteristics and agricultural practices related to native cotton cultivation. First, regarding gender distribution, the survey indicated that the majority of respondents were male (88.9%), while only a minority were female (11.1%). This gender disproportionality in participation may reflect broader societal norms or gender-specific roles within the communities, potentially influencing decision-making processes related to agricultural activities [22,30]. In terms of marital status, the survey highlights a predominance of cohabiting individuals (61.1%), followed by single (33.3%) and married (5.6%) respondents. This distribution suggests a diverse range of household structures within the communities, which could have implications for labor availability and resource allocation in cotton farming. Regarding experience cultivating native cotton in the Poyentimari area, the overwhelming majority of respondents (77.8%) reported having engaged in cotton cultivation. This high level of participation underscores the significance of cotton farming as a prevalent economic activity within communities, indicating a strong reliance on cotton for livelihoods [4,12,31].

Regarding housing conditions, the survey revealed that the majority of respondents owned their homes (94.4%), indicating a sense of stability and property ownership within the communities. Additionally, most households had access to basic services such as electricity (72.2%) and basic sanitation (88.9%), although a minority reported limited access to these amenities. Access to one’s own home, electricity, and basic sanitation is an important achievement for communities and guarantees the maintenance of communities in the countryside [6,7].

Education has emerged as a crucial factor influencing socioeconomic dynamics within communities. While the majority of respondents had completed primary education (27.8%) or secondary education (16.7%), a notable proportion had incomplete educational attainment across various levels. This highlights potential challenges related to access to quality education and its implications for economic opportunities and development within communities [7].

With respect to economic activities, agriculture, including native cotton cultivation, was the primary source of income for the majority of respondents (61.1%). This underscores the significant role of agriculture, particularly cotton farming, in sustaining livelihoods and local economies [4,32]. However, access to credit for cotton-related activities is limited, with the majority of respondents reporting no access to credit (83.3%).

The analysis of survey responses from the Poyentimari community sheds light on the intricate connections between demographics, past experiences, and current knowledge regarding native cotton cultivation [4,5,8,9]. We embark on a journey to explore these key findings and their potential implications for the future of cotton in this region—information that can guide future management of the crop within communities.

First, there was a notable association between the level of education (Question_20) and experience in native cotton cultivation in Echarati (Question_6). While the statistical tests did not reach significance at the chosen level (p values > 0.05), the contingency coefficient and Cramer’s V indicated a moderate to strong association. Individuals with higher levels of education, particularly those with complete primary or secondary education, are more likely to cultivate cotton in Echarati. This suggests that education might play a role in engaging individuals in agricultural activities, possibly through formal training or exposure to agricultural knowledge. Similarly, there was a strong positive association between education level (Question_20) and experience in native cotton cultivation in Echarati (Question_7). Statistical tests produced robust and significant results, indicating that individuals with higher levels of education are more likely to cultivate cotton in Echarati. This conclusion highlights the importance of education in shaping individuals’ involvement in agricultural practices, particularly in regions such as Echarati, where cotton cultivation is prevalent and specifically in Amazonian communities.

The relationship between cotton cultivation experience in Echarati (Question_6) and income source (Question_23) was examined, and the analysis revealed a statistically significant association. Individuals involved in agriculture, including native cotton cultivation, are more likely to have lower monthly incomes than those engaged in other income sources. This suggests that agriculture, while a significant part of the local economy, might not always provide substantial financial returns, highlighting potential socioeconomic challenges faced by cotton cultivators. A similar association was found between cotton cultivation experience in Echarati (Question_7) and income source (Question_23). The statistical tests suggest a moderate association, indicating that individuals with experience cultivating cotton in Echarati may have specific income sources compared to those without such experience. This finding underscores the importance of understanding the economic dynamics of cotton cultivation and its impact on household incomes in the region.

However, we present more nuanced findings regarding the relationship between cotton cultivation experience and monthly income. Although there are indications of a moderate association, the statistical tests do not provide enough evidence to establish a definitive connection. This suggests that factors beyond cotton cultivation experience might influence individuals’ income levels, emphasizing the need for further research to explore additional variables that contribute to household incomes.

The associations between cotton cultivation experience and beliefs about the demand for native cotton cultivation (Question_27) and the perceived use of native cotton (Question_28) were examined. The statistical tests revealed mixed results. While there are indications of a moderate association, particularly regarding perceived uses, the significance levels vary, suggesting complex dynamics at play. This underscores the importance of understanding local perceptions and beliefs surrounding agricultural practices, which can influence adoption and sustainability efforts.

Overall, the analysis of the questionnaire responses from the Poyentimari community revealed multifaceted relationships among demographic factors, experiences, and knowledge related to native cotton cultivation. The findings highlight the significance of education, income dynamics, and local perceptions in shaping individuals’ engagement with and understanding of cotton cultivation practices.

4.4. Comparisons between Communities

The communities of Chacopishiato, Koribeni, and Poyentimari share commonalities in their socioeconomic characteristics, agricultural practices, and the challenges they face with native cotton cultivation. These communities all possess favorable environments for the production, conservation, and maintenance of cotton species, yet their experiences reveal both similarities and distinctive patterns that shape their agricultural dynamics.

Agriculture is the primary source of income in all three communities, with native cotton cultivation playing a central role. This underscores the critical importance of agriculture not only for sustaining local economies but also for preserving crop diversity, similar to the findings in studies such as those by Pisani et al. [14,33] and Altenbuchner et al. [17]. However, the degree of reliance on agriculture varies among the communities, reflecting unique cultural practices and social norms, as noted in the studies of cotton farming in different global contexts. For instance, in the Koribeni community, higher participation in cotton-related activities may indicate stronger social cohesion and collective action, akin to the experiences of organic and BCI-licensed farmers in Madhya Pradesh, India, as documented by De Hoop et al. [15].

Access to essential services such as sanitation and electricity remains uneven across these communities, highlighting significant socioeconomic disparities. These challenges echo the broader issues faced by cotton-farming communities globally, where inadequate infrastructure and limited access to resources can impede economic development and quality of life, as seen in the case studies from Peru, India, and Tanzania (see section Theoretical Background). Addressing these disparities through targeted interventions is crucial for improving living conditions and supporting sustainable agricultural practices.

Education emerges as a pivotal factor influencing the socioeconomic dynamics of these communities, with varying levels of educational attainment potentially affecting economic opportunities. This mirrors the findings of Radhakrishnan [18], who emphasized the role of education in promoting sustainable farming practices and enhancing the livelihoods of cotton farmers. The gaps in educational access in these Peruvian communities highlight the need for policies that support educational initiatives, particularly those that can improve agricultural knowledge and skills [34].

Challenges such as limited access to quality seeds, technical knowledge, and credit for agricultural activities are prevalent across all three communities. These obstacles are consistent with those identified in other regions, where the availability of resources and training has been shown to directly impact productivity and resilience, as observed in the studies of organic and GM cotton farming [16]. The need for capacity-building initiatives is critical, not only to enhance productivity but also to preserve the genetic diversity of cotton species, ensuring long-term sustainability.

Household composition and marital status also vary among the communities, influencing labor availability and the organization of cotton farming activities. The greater proportion of married respondents in the Koribeni community, for example, may affect household dynamics in ways that are similar to the gender-related challenges observed in Tanzanian cotton farming by Altenbuchner et al. [17].

5. Conclusions

Community participation in activities related to native cotton is another area of divergence, with higher involvement in Koribeni and Poyentimari. This variation may reflect differences in social cohesion, which can significantly affect the success of collective agricultural efforts. The disparities in community perceptions and knowledge regarding native cotton cultivation further highlight the importance of culturally tailored interventions, particularly in technical training and knowledge transfer, as observed in various global contexts where local beliefs and practices shape agricultural outcomes.

Furthermore, this research makes a substantial contribution to understanding sustainability by exploring the conservation of genetic diversity in native cotton species. Emphasizing crop diversity preservation is crucial for maintaining ecological balance and ensuring the resilience of agricultural systems in the face of environmental challenges. The study’s focus on conserving native cotton in these communities aligns with global sustainability goals that prioritize biodiversity and the protection of traditional agricultural knowledge.

Additionally, this research highlights the importance of education and community involvement in the success of sustainable agricultural practices. It recognizes that promoting educational initiatives and enhancing social cohesion are essential for strengthening local capacities and ensuring the long-term sustainability of native cotton cultivation. The limitations of this study, including the sample size and the representativeness of the responses, could be addressed in future research by employing more diverse data collection methods and broader geographic coverage. These improvements would allow for a deeper understanding of how sustainable agricultural practices can be implemented and maintained in different rural contexts.