Learning models for enhancing farmers’ water management skills in rural areas of Sargodha, Pakistan

Análise de modelos de aprendizagem para aprimorar as habilidades de manejo da água dos agricultores em áreas rurais de Sargodha, Paquistão

ABSTRACT:

Since life on the planet, water has been claiming as a lifeline for human beings. The efficient use of water retains productive agriculture, environmental sustainability, poverty reduction, and economic growth. Manageable irrigation in Pakistan is considered as lowest amongst the world yet. Incredibly, the quality of groundwater is not merely declining by overexploitation but also getting worse. This study conducted in District Sargodha, Punjab-Pakistan, had the purpose of analyzing the impact of three farmers Learning Models such as the Value Management Model (VM), Collaborative Problem-Solving Workshops (CPSW) model, and Discovery Learning Model (DL). Particularly in materials and methods, a well-structured survey instrument was prepared for data collection. Both validity and reliability of the instrument were ensured before conducting the full-length study. Census study was designed because of not having a substantial trained population. A cross-sectional survey design was adopted for data collection, and data were collected from 92 trained farmers. In the analysis, both descriptive and inferential statistics were applied to analyse the data in SPSS. In descriptive, mean, standard deviation, rank, frequency, and percentage were computed. In Inferential, a paired t-test was computed to analyse the impact of training in enhancing small farmers’ income. The regression model was also computed to predict the variables such as farmers’ knowledge level before receiving training regarding Learning Models, Methods of Irrigation, Water Saving Technique, and different salt/nutrient and water measurement tools.

Key words:
learning models; value management; collaborative problem-solving workshop; discovery learning; on-farm irrigational management

RESUMO:

Desde que existe vida no planeta, a água tem sido considerada uma tábua de salvação para os seres humanos. O uso eficiente da água mantém a agricultura produtiva, a sustentabilidade ambiental, a redução da pobreza e o crescimento econômico. A irrigação administrável no Paquistão é considerada a mais baixa do mundo até o momento. Incrivelmente, a qualidade das águas subterrâneas não está apenas declinando devido à superexploração, mas também piorando. Este estudo, conduzido no distrito de Sargodha, Punjab-Paquistão, teve como objetivo analisar o impacto de três modelos de aprendizagem para agricultores, como o Modelo de Gestão de Valor (VM), o modelo de Oficinas Colaborativas de Resolução de Problemas (CPSW) e o Modelo de Aprendizagem por Descoberta (DL). Particularmente em relação aos materiais e métodos, um instrumento de pesquisa bem estruturado foi preparado para a coleta de dados. Tanto a validade quanto a confiabilidade do instrumento foram garantidas antes da realização do estudo completo. O estudo censitário foi concebido devido à falta de uma população treinada substancial. Um delineamento de pesquisa transversal foi adotado para a coleta de dados, e os dados foram coletados de 92 agricultores treinados. Na análise, estatísticas descritivas e inferenciais foram aplicadas para analisar os dados no SPSS. Na análise descritiva, foram calculados média, desvio-padrão, classificação, frequência e porcentagem. Na análise inferencial, foi calculado um teste t pareado para analisar o impacto do treinamento no aumento da renda de pequenos agricultores. O modelo de regressão também foi calculado para prever variáveis como o nível de conhecimento dos agricultores antes do treinamento sobre Modelos de Aprendizagem, Métodos de Irrigação, Técnicas de Economia de Água e diferentes ferramentas de medição de sal/nutrientes e água.

Palavras-chave:
modelos de aprendizagem; gestão de valor; workshop colaborativo de resolução de problemas; aprendizagem por descoberta; gestão da irrigação na propriedade

INTRODUCTION

Water is considered an essential input for productive and sustainable agricultural practices. Nature has blessed Pakistan with plenty of water resources, primarily groundwater reservoirs and the Indus Basin Irrigation System (IBIS). Indus Basin Irrigation System is a single contiguous and most extensive irrigation system in the world. Most of the farmers in Punjab use groundwater to fulfill their irrigation needs and using the flood irrigation method. Flood irrigation leads to the over-use of water, which causes water logging and decreasing groundwater quality due to over-exploitation. The water level is falling due to which water is getting worse (REHMAN et al., 2023). The knowledge level of farmers in Pakistan is comparatively low compared to world dynamics because of the technology transfer gap, lack of adaptation of new water-saving techniques, lack of adult education, and economic crises. Therefore, it is challenging for small-scale farmers to take innovative steps to improve their on-farm water management skills. Also, there is a strong link between irrigation, crop production, and poverty reduction. The use of conventional irrigation methods and poor agronomic practices often leads to the overuse of water. Farmers with small to medium holdings have had little access to irrigation technologies, as affordable and accessible tools are neither produced nor widely distributed in Pakistan. Conversely, literature described that irrigated land has been increased from 40 million to more than 260 million hectares in the world. QURESHI (2011) in his study described that due to the increased population in Pakistan, it is estimated that by 2025, the country will face approximately 32% water shortage that will result in almost 70 million of tones food shortage. The author further described that due to climate change and reduction in water storage reservoirs by 2025 the capacity to store ground water will reduce to 30%. ROSEGRANT & CAI (2002) pointed out in their study that irrigation water demand for agriculture and other sectors utilization will be increased up to 13.6% in 2025. Thus, irrigation is critical to Pakistan’s food security, poverty reduction, and economic growth, yet irrigation profitability in Pakistan is considered very low amongst the world (AZAD et al., 2003). The main extension challenge is how to scale out existing and new technologies over the vast irrigated areas of Pakistan and develop skills and capacity among farmers to manage and maintain profitable irrigation systems.

Extension approaches to farming in Pakistan occur in two ways: the traditional top-down, expert-to-farmer approach; and the interactive Farmer Field School (FFS) approach. FFS programs are used to transfer specialist knowledge, promote skills, and empower farmers. Democratic approaches to enhancing on-farm water management practices are a very suitable way to improve farmers’ water management skills. Without using FFS approaches, farmers’ knowledge cannot improve (WADDINGTON et al., 2014). Participants in the process of social learning share diverse opinions and experiences in the relevant field, and as all the actors would have different types of knowledge, water management can be enhanced (SCHUSLER et al., 2003). In social learning, positive points can be seen that different people and organizations have different backgrounds. Here, they have a chance to learn multiple methods to reserve and use water sensibly.

Similarly, the chances of new ideas and methods may arise slowly and gradually, significantly reducing the threat of increasing water scarcity (RIST et al., 2006). Water management issues can be resolved by involving people and some local and international actors in the attempts. In this way, there would be a learning process introduced to different areas. There would also be knowledge sharing in this environment, which would help prevent this problem from worsening (ISON et al., 2007).

Malcolm Shepherd Knowles (1913-1997) was an American educator well known for using the term andragogy as synonymous with adult education. According to Malcolm Knowles, andragogy is the science and art of adult learning; thus, it refers to any form of adult learning (SAIDOV et al., 2023). The term learning refers to the process of getting and understanding something by studying or through experience. The designed learning models were used to engage farmers in improving their knowledge regarding water management skills by conducting training at their farms. There are three specified kinds of farmers learning models, introduced by the scientific collaboration of the Australian Center for International Agriculture Research (ACIAR) and the University of Canberra (UC). These farmers leaning models are;

1. Value Management (VM)

2. Collaborative Problem-Solving (CPS)

3. Discovery Learning (DL)

Value Management is a formal, highly analytical, standard-based, and Australian endorsed value-focused approach which meets the Australian Value Management (VM) Standard. This model identifies the rigorous problems and delineation of critical issues and concerns. The design of ‘value propositions, as identified by OSTERWALDER et al. (2014), creates products and services that clients’ want. CPS model is based on collaboration for solving problems related to concern issues like irrigational management. This model is a research-based intervention method called Organic Research and Collaborative Development (ORCD), created by (SPRIGGS & CHAMBERS, 2011). ORCD respects all participants as equals and experts in their areas to surface the fundamental issues that need to be addressed. The discovery learning model is different from other formal and guided learning methods because it relies on discovery insights and generalizations from instances rather than general principles. DL model is both an individual and group method which originated from the work of educators such as BRUNER (1961), FREIRE (1984), and DEWEY (1997). In this study, DL utilized demonstrations only for different water, soil, and nutrients measurement tools such as Tensiometer, Full Stop Wetting Front Detector, Chameleon Soil Water Sensor, and Nitrate Test Strip and Pocket EC meter.

Water management is one of the key dimensions of sustainable agricultural practices. This is more relevant in water scarce regions such as Sargodha in Pakistan. In these remote locations, farmers are unskilled in how to make good use of the irrigation water, leading to wrong application of water use that threatens crop productivity and income (PEREIRA et al., 2002). This research addresses this problem by looking into different learning models so as to find and put in place appropriate measures to improve water management capabilities of Sargodha farmers. Learning models interventions are effective and relevant to save the irrigation water and improve the incomes of local farmers (NIKOLAOU et al., 2024). This research seeks to comprehensively understand the relationship between training and water management practices in the studied area and provides educational responses aimed at helping farmers to use water in better way in agriculture. Other accompanying research questions are as follows; 1) which changes in the rural contexts have improved the management aspects of water resources through the use of learning models? 2) how these models could be made effective in Sargodha region, and 3) what are the constraints being faced by local farmers of Sargodha adopting water management skills. To address these questions, current study was planned on the objectives which include; 1) to study the demographic profiles of the respondents, 2) to appraise the awareness level of farmers regarding learning models, 3) to assess farmers’ perceptions regarding the adoption of on-farm water management competencies through learning models, 4) to identify the constraints faced by farmers in the adoption of on-farm water management skills through learning models, 5) and to assess perceptions of the farmers regarding to operate different water/ nutrients, and salt measurement tools through the discovery learning model. The overall goal of the research was to assess the knowledge level of farmers regarding learning models, water management techniques/methods, water/nutrient, and salt measurement tools before and after receiving training.

MATERIALS AND METHODS

Cross-sectional survey was conducted for data collection. The census research method was adopted for the current study due to having manageable and limited population, which is assumed to be more accurate and robust since it covers all the respondents of the population and eliminates the sampling error (DANIEL, 2011).

Population

The population size of 92 trained respondents on different learning models was used in this study.

Instrumentation

A well-structured survey instrument (interview schedule) was prepared. The validity and reliability of the instrument were ensured before conducting the full-length study. Also, validity was ensured by using the face and content methods. Furthermore, reliability was ensured by computing Cronbach’s alpha value in SPSS. A 5-point Likert-type scale was used to assess the respondents’ awareness, perception, and knowledge level.

Data collection and analysis

In current study, both descriptive (means, standard deviations and ranks) and inferential (e.g., regression analysis) statistics were used to interpret the results and research objectives. A cross-sectional survey design was adopted for data collection. Face-to-face interviews of the respondents were arranged during data collection. In the analysis, data were coded in SPSS, and both descriptive and inferential statistics were computed to analyze the data. The researchers used paired t-test in the study for the better understanding of farmers response, and further to infer the significant differences in the response of different populations of farmers. Further, since in this study the observations (farmer’s response) were obtained in pairs; therefore, to note down the differences in means response of farmers this test considered robust (RASCH et al., 2011). The paired t-test is further applied in situations where response is taken from the same individuals (in this case were the farmers) to determine mean difference in paired responses is statistically different or not (AFIFAH et al., 2022). While, the reason behind choosing regression analysis was to develop decision support model for giving accurate predictions in future to save water in the studied area specifically and in Pakistan generally. It is evident from the literature that regression models could help providing effective understandings and predictions in agriculture systems which are smart in nature (VIRNODKAR et al., 2020). The regression model developed in this study could be very fruitful for the local farmers in planning their irrigations effectively (El BILALI et al., 2020).

RESULTS AND DISCUSSIONS

Demographics

The respondents’ demographic characteristics such as age, gender, education, number of crops sowing per year, experience, agriculture landholding, family types, annual income from agriculture before and after training, and income source were assessed. Data were collected regarding respondents’ demographic characteristics because it is considered a dire need to explain the study’s entire population (ASHRAF et al., 2018). The interpretation and discussion regarding demographic results are explained in following table 1 and table 2.

Table 1
Frequency distribution of demographics characteristics of the respondents.

Demographics Information	Frequency (F)	Percentage (%)
--------------------------------------------------------------------Age groups in (years)----------------------------------------------------------------------
≤ 25	7	7.6
26-40	51	55.4
41-55	22	23.9
56-65	7	7.6
Above 65	5	5.4
----------------------------------------------------------------No. of Crops sowing per year----------------------------------------------------------------
1	23	26.2
2	53	58.8
3	16	17.8
------------------------------------------------------------------Experience groups (years)------------------------------------------------------------------
≤ 10	29	31.6
11---20	32	34.8
21---30	18	19.6
31---40	9	9.8
41---50	4	4.2
-------------------------------------------------------------Agricultural Landholding (acres) --------------------------------------------------------------
≤ 10	55	57.2
11-20	29	31.37
21-30	3	3.3
31-40	1	1.1
41-50	2	2.2
Above 50	2	2.2
---------------------------------------------------------------------------Family Type-------------------------------------------------------------------------
Nuclear	27	29.3
Joint	45	48.9
Extended	20	21.7
--------------------------------------------------------Source of Income other than Agriculture-----------------------------------------------------------
Business	14	15.2
Job	10	10.9
Any other (agriculture)	68	73.9

Table 2
Cross-tabulation of the demographic factors of the respondents such as educational level and gender.

Gender	-------------------------------------------------------------------------Education Level------------------------------------------------------------------------
		No formal edu.	Up to 8th grade	*SSC	HSSC.	Grad.	Post- Grad.	Total
	Male count	9	34	27	7	3	2	82
	% with gender	11.0	41.5	32.9	8.5	3.4	2.4	100%
	Female count	0	10	0	0	0	0	10
	% with gender	0	100	0	0	0	0	100%
Total	Count	9	44	27	7	3	2	92
% with gender	9.8	47.8	29.3	7.6	3.3	2.2	100%

• ^*SSC= Secondary School Certificate, HSSC = Higher Secondary School Certificate.

Table 1 quantifies the respondents’ demographics characteristics, depicts that only seven farmers were in the ≤ 25 years of age group. Also, 51 farmers fell in between the age group of 26-40 years. Twenty-two farmers were in the age group of 41-55.Sevenwere in the age group of 56-65 and only five were above 65. Furthermore, only 16 farmers cultivated three crops per year, 53 planted two, and 23 farmers sowed only one crop per year. Data revealed that 29 respondents had had less than or equal to ten years farming experience, 32 farmers between 11-20 years, 18 respondents in the range of 21-30 years, nine between 31-40 years, and only four farmers have experience over 40 years in agricultural and irrigational management. Exploring the respondents’ agricultural landholding possession, 55 had less than or equal to 10 acres, 29 were lying in between the group of 11-20 acres, and the other eight respondents have an agricultural landholding of above 20 acres. Interpreting the results about other demographics characteristics such as family type and income source other than agriculture, 27 farmers belonged to a nuclear family, 45 farmers were from a joint family, and 20 belonged to an extended family type. Finally, 14 respondents were also doing business, ten were doing jobs, and 68 farmers only engaged in agriculture.

Table 2 describes the cross-tabulation of gender and different level of education. Data showed that nine male respondents had no formal education at all, 34 possessed up to eighth grade level of education, while 27 held SSC, seven male respondents possessed HSSC. In addition, three had graduate level education, and only two respondents attained a post-graduation level of education. Particularly about female respondents, only ten female farmers were included in this study; the yall had up to eighth level of education.

General awareness of the farmers for learning models

After analyzing the demographic factors, respondents’ awareness level regarding three learning models was assessed and quantitative results are presented in the following table 3. The awareness level of the respondents was ranked on the bases of the mean response of the respondents asked for each statement during the survey.

Table 3
Means, Standard Deviations and Ranking about general awareness level of the trained farmers regarding learning models.

General Awareness regarding learning models	n	*Mean	SD	RANK
Learning Models	92	3.81	0.85	13
Benefits of learning models	92	4.18	0.78	2
Role of learning models in achieving better water management skills	92	4.03	0.85	9
Interactive workshops of learning models are essential for improving water management skills	92	4.08	0.82	6
Learning models lead towards community development	92	3.84	0.75	14
Farmers participation in learning models is necessary	92	4.20	0.80	1
Learning models are effective to solve water-related problems	92	4.05	0.81	8
Irrigation should be according to crops requirement	92	4.11	0.66	5
Water management through learning models is necessary to enhance crop production	92	4.15	0.78	3
Importance of learning models for improving on-farm water management skills	92	4.14	0.87	4
Enhancing crop productivity through learning model’s implementation	92	4.00	0.75	10
Encouraging advanced water management practices replacing conventional irrigation methods through learning model’s application	92	4.06	0.91	7
Capacity building of farmers in maintaining profitable irrigation systems through learning models	92	3.98	0.85	11
Demonstration regarding water management tools at Virtual Irrigation Academy website	92	3.89	0.89	12

• ^*Mean:1= Not at all aware, 2= Slightly aware, 3= Somewhat aware, 4= Moderately aware, 5= Extremely aware.

The section was designed to assess the general awareness of learning models by the trained respondents for developing their water management skills. Table 3 depicts the results for this factor. The attributes were explained in terms of rank, standard deviation and mean value, and n=sample size in the given dataset. The statement “farmers’ participation in learning models is necessary” is ranked at first with a mean value of 4.22, which indicated that they were moderately aware of the importance of farmers’ participation in training and implementation of learning models, that had been insured since the inauguration of the training by all project team members and researcher. It would not be wrong to say that encouraging farmers to attain their maximum participation during training is indispensable to exploring their traditional knowledge, which might help improve their on-farm water management competencies. The second attribute is the benefits of learning models with a mean value of 4.18, which shows that they were moderately aware of the benefits of learning models’ implementation.

After analyzing the general awareness of the farmers regarding learning models, their awareness level for specific learning model (Value management, Collaborative Problem solving workshop and Discovery learning) was also analyzed to highlight their comprehension for these models.

Awareness level of the farmers in Value Management Model

Secondly, the awareness level of the farmers for Value management model was analyzed and the results are presented in the following table 4.

Table 4
Means, Standard Deviations and Ranks about awareness level of the trained farmers regarding Value Management model and its concept.

Awareness level of the respondents for Value Management Model	n	*Mean	SD	RANK
VM model is value-focused approach regarding water management	35	4.11	0.90	7
Effective utilization of existing water resources through VM model	35	4.31	0.75	1
VM model is formal, highly analytical and standard base regarding water management	35	4.12	0.52	6
Identification of stakeholders for better model implementation for irrigation management	35	3.98	0.90	10
Importance of sharing information regarding water management in VM model	35	4.25	0.78	3
Ideas generation about irrigation water management concerns particularly in VM model practice	35	4.17	0.70	4
Ideas generation about irrigation in VM model	35	3.88	0.67	11
Divergent and convergent phases concept regarding water management in VM model	35	3.77	0.91	13
Develop best option to improve irrigation water management skills in VM model	35	3.85	0.10	12
Provide line for preparation of action plan regarding water management in VM model	35	4.02	0.74	8
To address the statement of the problem for irrigation management in VM model implementation	35	4.16	0.70	5
De Bono Six Hats thinking technique regarding irrigation management in VM model	35	4.00	0.90	9
SWOT analysis in VM model	35	4.28	0.71	2

• ^*Mean: 1= Not at all aware, 2= Slightly aware, 3= Somewhat aware, 4= Moderately aware, 5= Extremely aware.

Only 35 selected farmers were trained for Value management model; therefore, the sample size to answer this factor was (n=35). According to the results from table 4, the statement “effective utilization of existing water resources through VM model” is ranked first, having the highest mean value of 4.31. Conversely, the statement “divergent and convergent phases’ concept regarding water management in VM model,” ranked as 13^thhaving the mean value of 3.77, and this value falls in between the scale of somewhat aware to moderately aware. All values given in table 4 show that farmers’ awareness level increased after receiving training. The lowest mean found in the analysis for trained farmers in Value management model is 3.77 which clearly showed that training has good impact in enhancing the awareness level of the selected farmers regarding the VM model and its importance for their water management skills.

Awareness level of the farmers in Collaborative Problem Solving Model

Next, the 33 selected farmers were assessed in CPS model and the results are presented in the following table 5.

Table 5
Means, Standard Deviations and Ranks about awareness level of the trained farmers regarding CPS concept.

Awareness level of the respondents Collaborative Problem Solving (CPS)	n	*Mean	SD	RANK
Importance of grouping to share ideas regarding water management strategy in CPS model	33	3.72	0.87	5
To gather participant’s information regarding prevailing irrigation issue in CPS model practice	33	3.66	0.64	7
Importance of collaborative discussion to improve water management skills in CPS model	33	3.84	0.87	3
Exploration of issues and resources for irrigation management in CPS model	33	3.69	0.88	6
Boost up overnight thinking process regarding water management in CPS model	33	3.36	1.11	8
Refining/ prioritizing the issues in CPS model	33	3.75	0.86	4
Preparing the action plan for implementation of CPS model	33	4.18	0.46	1
Monitoring and evaluation plan for irrigation management in CPS model	33	3.90	0.94	2

• ^*Mean: 1= Not at all aware, 2= Slightly aware, 3= Somewhat aware, 4= Moderately aware, 5= Extremely aware.

Table 5 shows that third factor of objective two was asked from only those respondents who had received training regarding the implementation of CPWS. Out of 92 respondents, only 33farmers were trained in CPS model. The results showed that according to the respondents “Preparing the action plan for implementation of CPS model” ranked first with the highest mean value of 4.18, which falls at moderately aware level on Likert type scale. The statement of “boost up overnight thinking process regarding water management in CPS model” ranked as eight hand recorded as the lowest mean of 3.36. The overall results show that respondents’ awareness level for CPS model was recorded from “somewhat aware” to “moderately aware” after receiving training. Every individual was found aware regarding the implementation of his/her specific learning model.

Awareness level of the farmers in Discovery learning Model

Similarly, the 24 selected farmers were assessed in Discovery learning model after training and asked different statements during the survey to assess their awareness level on five-point Likert -type scale. The results are presented in the following table 6.

Table 6
Mean, Standard Deviations and Ranking of awareness level of the trained farmers regarding Discovery learning model.

Awareness level of the respondents in Discovery Learning Model	n	*Mean	SD	RANK
Soil test through discovery learning models to measure nutrients, salts and water contents	24	4.08	0.92	6
Discovery learning model implementation	24	4.45	0.77	1
DL model is individual as well as collaborative based applied method	24	4.13	0.53	4
Tools for irrigation management and its demonstration through discovery learning	24	4.12	1.03	5
Use of water and nutrients measurement tools through discovery learning model	24	4.33	0.81	2
DL model helps in operating water measurement tools	24	4.25	0.67	3

• ^*Mean:1= Not at all aware, 2= Slightly aware, 3= Somewhat aware, 4= Moderately aware, 5= Extremely aware.

The fourth factor of the objective two results showed that respondents’ awareness level regarding the discovery learning model’s implementation was considered good since all of them were fallen at “moderately aware” level on five-point Likert-type scale. This factor was related to only those farmers who had received training in discovery learning model related to operating and installing different field tools. The highest and lowest mean values were recorded at 4.45 and 4.08, respectively. Both values fall at 4=moderately aware on the Likert-type scale. The statement which ranked at first and last are “discovery learning model implementation” and “soil test through discovery learning model to measure nutrients, salts, and water contents” respectively.

Perceptions of the farmers regarding adoption of water management skills

The third objective of the study was to assess the perceptions of the respondents regarding adoption of water management skills through different learning models in the study area. This section was assessed on 5-point Likert-type scale from “strongly disagree” to “strongly agree”. The results are presented in the following table 7.

Table 7
Mean, Standard Deviations and Ranks about perceptions of the trained farmers regarding adoption of water management skills through learning models.

Perception of the Respondents regarding adoption of water management skills after having training in Learning models	n	*Mean	SD	RANK
Learning models are helpful to improve on-farm water management skills	92	4.07	0.82	10
Learning models contribute to enhancing water management skills	92	4.22	0.82	3
Learning models encourage participation of farmers for improving water management practices	92	4.16	0.71	8
Learning models’ applications are necessary for enhancing farmers’ water management skills	92	4.09	0.82	9
Learning models for enhancing farmers water management skills is a participatory approach in practice	92	4.18	0.72	6
Learning models identify issues regarding water management practice	92	3.93	0.67	12
Active participation of both men and women is necessary in learning models for better water management	92	3.90	0.99	13
Active participation to learn these models regarding water management leads towards community development	92	3.97	0.78	11
Use of the latest water management tools also increase agricultural productivity	92	4.21	0.73	4
Learning models help in problems identification regarding water management	92	4.19	0.69	5
Learning models encourage to tackle water management problems as compared to traditional practices	92	4.17	0.65	7
Learning models are helpful for reducing the water losses	92	4.31	0.69	2
Learning models’ implementation reduces irrigational issues of farmers	92	4.44	0.70	1

• ^*Mean: 1=Strongly disagree, 2=Disagree, 3= Neither agree nor disagree, 4=Agree, 5= Strongly agree.

The above table shows that almost all respondents gave their opinion on five-point Likert-type scale and fall at level 4=Agree. Therefore, it is inferred that farmers agree with all given statements. The statements which ranked at first by the respondents is “learning models’ implementation reduces the irrigational issues of the farmers” highlighted the significance of the water management issue for irrigational purpose at the time of water scarcity and climate change in the world. The other statement which ranked at 13^th by the respondents is “active participation of both men and women is necessary in learning models for better water management” showed that how important is gender-wise participation of the farmers in the training for learning of water management skills. Findings of this study agree with (XIULING et al., 2023) who reported that farmer’s technical trainings play vital role in water management skills of the farmers; since, trainings change the behavior of the farmers and lead them to adopt water saving irrigation technologies. This is also important to mention that trainings also save farmers time which they generally spend in searching information, and ameliorate their level of cognition. Since, learning models address the constraints of the farmers including land, labor and capital and tell the solutions how to control them, therefore lead towards water management skills. Moreover, the learning models are more targeted and provide easy knowledge, solutions, technologies which really help farmers to save their quality irrigation water (XIULING et al., 2023).

Constraints faced by farmers in adoption of water management skills

Both Value management and Collaborative problem solving models have found lot of commonalities at implementation stage. Therefore, the factor of “constraints faced by the farmers during adoption of water management skills” was asked and analyzed only for 68 farmers out of 92 those were trained for applications of these two models for adoption of water management skills. Different constraints were identified and asked during the survey from respondents and results are presented in the following table 8.

Table 8
Means, Standard Deviation, and Ranks for constraints faced by the respondents in adoption of water management skills.

Constraints	N	*Mean	SD	RANK
Lack of financial resources	68	4.33	0.87	1
Lack of complete awareness	68	3.83	0.82	3
Lack of Accessibility	68	3.39	0.88	7
Lack of proper advisory services	68	3.20	0.85	10
Technical constraint at the time of application and noting readings	68	2.60	0.88	13
Lack of technical knowledge	68	3.26	0.85	8
Lack of active learning	68	3.22	0.91	9
Lack of interest	68	3.17	0.80	11
Lack of training facilities	68	3.57	0.95	6
Lack of time to participate in the field application	68	2.95	1.17	12
Lack of motivation	68	3.88	0.95	2
Lack of relevant material	68	3.64	0.92	5
Lack of relevant program	68	3.70	1.02	4

• ^*Mean: 1=Not a constraint, 2=Somewhat constraint, 3=Moderate constraint, 4= sure constrain, 5=Huge constraint.

Table 8 shows that the lack of financial resources was the highest constraint faced by respondents to adopt water management skills to manage their on-farm water resources since the mean score was 4.33 which fall at level 4= sure constraint on 5-point Likert-type scale and ranked first. Regarding the “technicalities at the time of application and noting readings of the instruments” ranked 13^th with the lowest mean value of 2.60, which falls in between the “somewhat constraint to moderate constraint” on 5-point Likert-type scale. Lack of motivation ranked second with the mean score of 3.88. As a whole, the results showed that respondents faced moderate level constraints to sure constraint in adoption of water management skills in learning through Value management and Collaborative problem solving models. These models provide very sustainable mechanisms to overcome these constraints; since, these models have been tested in different countries and proved to be very effective in saving water and improving the income of the farmers (BARMAN et al., 2017). In this study, the farmers were agreed that these models will surely help them settling their irrigation issues, saving water loss and enhancing their water management skills. Since, saving water through these models will save farmers resources/input cost, which will motivate them and bring positive change in their behavior, in this sense these models become sustainable. Moreover, opting these models will also increase their crop productivity and in turn income enhancement, in this way these models will become compulsion to be followed by the farmers because these would bring positive change in their lives. This gives the surety about the sustainability of these models impacts (KIPTOT & FRANZEL., 2019). In Pakistan, normally the farmers learn from the experiences of other farmers. When some farmers will opt these models and will get benefit from them regarding their income increase and conservation of resources, this will influence the other farmers to opt for the same models, which will lead towards the sustainability of the impacts of these models (SHAH et al., 2020; SHAHID et al., 2022).

The second factor was relevant to only those respondents who were trained regarding the DL model only. Therefore, it was open to conducting interviews only from 24 respondents out of 92. Table 9 shows that farmers were facing the least constraints to operate distributed and installed tools at their field. Meanwhile, it was recorded that respondents were facing hurdles in conducting nitrate strip tests to measure the soil nutrients. The highest mean value among mentioned constraints was recorded at 2.12, which fell in between the Likert-scale of somewhat constrained to a moderate constraint. Respondents were facing the least constraint regarding operating Tensiometer with the mean value of 1.50, which falls on the scale of 1=not a constraint.

Table 9
Means, Standard Deviation, and Ranking of the specified constraints faced by the specified respondent in the study area.

Constraints	N	*Mean	SD	RANK
To operate Chameleon Soil Water Sensor	24	1.79	0.77	4
To operate Full Stop Wetting Front Detector	24	1.87	0.74	3
To operate Tensiometer	24	1.50	0.58	5
To conduct Nitrate Test Strip (Measuring Nutrient)	24	2.12	0.74	1
To operate Pocket EC meter (Electrical conductivity) (Measuring Salt)	24	1.91	0.77	2

• ^*Mean: 1=Not a constraint, 2=Somewhat constraint, 3=Moderate constraint, 4=Constrain, 5=Huge constraint.

Table 10 reveals that respondents agreed with all the attributes given. The statements which are ranked at first and last with the mean values are given below. Uses of water measurement tools give us confidence and experience (Mean = 4.50); Water management tools improve farmers’ technical knowledge (Mean = 4.16), respectively. These two mean values show that most respondents agree with all given statements of the table, and values fall in between the Likert scale of 4=agree to 5=strongly agree.

Table 10
Means, Standard Deviation, and Ranking of the specified constraints faced by a specified respondent in the study area.

Perception of the Respondents about tools	N	*Mean	SD	RANK
Water conservation tools are highly effective for improving water management skills	24	4.25	0.84	8
Uses of water measurement tools give us confidence and experience	24	4.50	0.65	1
Water measurement tools are easy to handle	24	4.33	0.56	3
Water management tools provide precise status of water availability	24	4.30	0.80	5
Water measurement tools are easy to operate	24	4.28	0.62	7
Water measurement tools help in time-saving as well	24	3.95	0.69	11
Tools have prime importance in water management	24	4.29	0.69	6
Use of water management tools is the best way of discovery learning	24	4.20	0.72	9
Active participation of farmers to operate water measurement tools is necessary to improve management skills	24	4.45	0.58	2
Without improving farmers’ water management skills and relevant technical knowledge, water cannot be conserved	24	4.35	0.56	3
Water management tools improve the technical knowledge of farmers	24	4.16	0.63	10

• ^*Mean: 1=Strongly disagree, 2= Disagree, 3=Neither agree nor disagree, 4=Agree, 5=Strongly Agree.

The study’s sixth objective was to assess the respondents’ knowledge before and after receiving training about different factors. Nevertheless, the results are interpreted according to the data set before receiving training from the respondents. The respondents’ knowledge level was assessed on 5-pointLikerttype scale “1= not at all knowledge to 5= very good knowledge”. Mean will be discussed in between of these Likert scales during result interpretation. This objective was divided into four further factors. The first factor was related to learning models, the second was irrigation methods, the third was related to water-saving techniques, and the last related to water, salt, and nutrient measurement tools.

Table 11 shows that farmers had very little knowledge about learning models and their implementation specifications before receiving training. The results revealed that the highest mean falls between the Likert-type scale of little knowledge to moderate knowledge. The highest recorded mean value of 2.27 shows that farmers had little knowledge about collaborative problem solving before receiving because due to traditional and cultural inheritance, the people of the village area always would like to solve their issue collectively. All other indicators showed that farmers were unacquainted with value management, learning models, discovery learning, and the importance and implementation of learning models. The lowest mean value of 1.31 ranked at sixth showed that farmers were not knowledgeable about learning models because most of the respondents said that this was the first time they engaged with learning activities like workshops and training.

Table 11
Means, Standard Deviation, and Ranking about the Knowledge level of the respondents before receiving training regarding leaning models, methods of irrigation, water-saving techniques, and conservation tools.

Learning Models	N	*Mean	SD	RANK
Learning models	92	1.31	0.55	6
Implementation of learning models	92	1.36	0.62	5
Discovery learning model	92	1.45	0.77	4
Value Management model	92	1.81	0.69	2
Collaborative problem-solving workshop	92	2.27	0.86	1
Importance of learning Models	92	1.71	0.95	3
-------------------------------------------------------------------Methods of Irrigation-----------------------------------------------------------------------
Drip Irrigation method	92	2.08	0.94	5
Sprinkler irrigation method	92	2.18	0.91	4
Furrow irrigation method	92	2.90	0.96	3
Flood irrigation method	92	3.78	0.83	1
Basin irrigation method	92	3.53	0.96	2
-----------------------------------------------------------------Water-Saving Techniques--------------------------------------------------------------------
Water harvesting	92	1.85	0.89	9
Irrigation Scheduling	92	2.38	1.00	6
Drought Tolerant Crops	92	2.31	1.14	8
Rotational Grazing	92	2.46	0.89	5
Mulch and Compost Technique	92	2.56	1.09	4
Cover Crops	92	2.34	0.94	7
Conservation Tillage	92	2.57	0.90	3
Organic Farming	92	2.78	1.10	1
Supplemental irrigation	92	2.65	1.05	2
------------------------------------------------------------------------------Tools-------------------------------------------------------------------------------
Chameleon Soil Water Sensor	24	1.33	0.56	4
Full Stop Wetting Front Detector	24	1.32	0.56	5
Tensiometer	24	1.37	0.76	3
Nitrate Test Strip (Measuring Nutrient)	24	1.79	0.72	2
Pocket EC (Electrical conductivity) (Measuring Salt)	24	2.25	0.94	1

• ^*Mean: I=No knowledge at all, 2=Little knowledge, 3=Moderate knowledge, 4= Good knowledge, 5=Very good knowledge.

Table 11 shows that before training, knowledge level of farmers was not very good. The mean value off iveon the Likert scale suggested the flood and basin irrigation methods ranked first and second, with mean values of 3.78 and 3.53 indicate good knowledge. Conversely, the value also shows that farmers had little knowledge about other irrigation methods, such as drip and sprinkler irrigation methods.

Table 11 also shows that organic farming and supplemental irrigation ranked first and second respectively, with the highest mean values of 2.78 and 2.65, which are between little to moderate knowledge level. Water harvesting and drought-tolerant crops ranked at ninth and eighth with the lowest mean values of 1.85 and 2.31, which fall in between the scale of little knowledge to moderate level of knowledge. Values about different tools also show that farmers had very little knowledge about different tools before receiving training. Means values for all variables fall in between the scale of knowledge at all to little knowledge. The N=24 clearly shows that only 24 respondents fell under the criteria to conduct their interview.

Table 12 clearly shows that training positively impacts the respondents’ knowledge level regarding different learning models. Mean values indicate that after receiving training, the respondents’ training level has increased from little knowledge to very good knowledge. Furthermore, training pieces have a good impact in enhancing the respondents’ knowledge regarding different irrigation methods. The other values in table 12 show that training positively impacts the respondent’s knowledge regarding different water-saving techniques. Mean values can interpret that after receiving training, the respondents’ knowledge level increases from little knowledge to very good knowledge. The remaining values also depict that training significantly impacts the respondent’s knowledge regarding different water, nutrient, and salt measures. The mean values recorded and interpreted at 4.55 highest for chameleon soil water sensor and 4.33 lowest for nitrate test strip, both mean value witness and fall in between Likert-scale of good knowledge to very good knowledge level.

Table 12
Means, Standard Deviation, and Ranking about the Knowledge level of the respondents after receiving training regarding leaning models, methods of irrigation, water-saving techniques, and conservation tools.

Learning Models	N	*Mean	SD	RANK
Learning models	92	4.20	0.79	4
Implementation of learning models	92	4.19	0.77	5
Discovery learning model	92	4.18	0.63	6
Value Management model	92	4.25	0.63	3
Collaborative problem-solving model	92	4.50	0.52	2
Importance of learning Models	92	4.65	0.54	1
-----------------------------------------------------------------------Methods of Irrigation--------------------------------------------------------------------
Drip Irrigation method	92	4.28	0.76	5
Sprinkler irrigation method	92	4.29	0.60	4
Furrow irrigation method	92	4.60	0.57	3
Flood irrigation method	92	4.75	0.45	2
Basin irrigation method	92	4.82	0.40	1
--------------------------------------------------------------------Water-Saving Techniques------------------------------------------------------------------
Water harvesting	92	4.18	0.60	9
Irrigation Scheduling	92	4.33	0.69	7
Drought Tolerant Crops	92	4.28	0.63	6
Rotational Grazing	92	4.35	0.63	5
Mulch and Compost Technique	92	4.39	0.62	4
Cover Crops	92	4.26	0.72	8
Conservation Tillage	92	4.51	0.73	2
Organic Farming	92	4.44	0.56	3
Supplemental irrigation	92	4.64	0.54	1
--------------------------------------------------------------------------------Tools------------------------------------------------------------------------------
Chameleon Soil Water Sensor	24	4.55	0.65	1
Full Stop Wetting Front Detector	24	4.36	0.70	4
Tensiometer	24	4.37	0.49	3
Nitrate Test Strip (Measuring Nutrient)	24	4.33	0.63	5
Pocket EC (Electrical conductivity) (Measuring Salt)	24	4.54	0.50	2

• ^*Mean: I=No knowledge at all, 2=Little knowledge, 3=Moderate knowledge, 4= Good knowledge, 5=Very good knowledge.

In present study, collaborative problem-solving workshop model effect was significant. There are reports that this model is very exciting training approach and produces rewarding results. The reason behind that may be, this model affects critical thinking of the respondents, and in this way changes attitudes and cognition skills of the trainees (in this case were the farmers). Moreover, it explores problems with inadequate structure in real-world scenarios with the learners as its main focus. It can motivate the respondents to reach their full potential as problem solvers, which will greatly enhance their attitude toward problem solving (XU et al., 2023). These could be the possible reasons why this model proved to be efficient in this study. The model which remained second good in training the farmers and they were willing to adopt this model was Value Management model. The potential justification behind this may be because this model has been designed for the farmers having small-holdings. Since, in present study the farmers chosen were having small-holdings; therefore, this model remained second effective model in training of the respondents/farmers. Secondly, this model directly addresses issues pertaining to productivity of the crops and income of the farmers and provides very workable solutions to resolve those issues. Therefore, the model was of great interest for the farmers and they ranked its importance second in their training (FOFANA et al., 2020).

The statistical oriented values in table 13 show that there is a significant difference found among mean values regarding small-scale farmers’ income before and after receiving training. However, farmers’ training at their farms using participatory approaches by facilitator trainers has a significant impact in improving their annual income.

Table 13
Paired Sample Statistics.

Pair 1	Mean	N	Std. Deviation	Std. Error Mean
AIBRT	878043.47	92	1276342.42	138067.89
AIART	922608.69	92	1421573.42	146209.27

In table 14, the P = 0.007 clearly shows that a highly significant difference was found in the income levels of the respondents after receiving training. Hence, “Ho = There is no difference in the respondents’ income level after receiving training” was rejected and the alternative hypothesis “H1 = There is a difference in the respondents’ income level after receiving training” was retained.

Table 14
Paired Sample Test.

	---------------------------------------Paired Difference-------------------------------------------
	Mean	Std. D.	Std. Error Mean	95% confidence interval of the difference
				Lower	Upper	t	df	Sig.
Pair 1 AIBRT -AIART	-114565.21	394501.16	41129.63	-196264	-32866.24	-2.785	91	.007

Table 15 shows that the regression model was computed, putting independent factors such as the respondents’ knowledge level regarding; learning models, irrigation methods, water-saving techniques, and measurement tools before receiving training. The model-oriented results showed the significant 5% level of significance as F(4,24) = 10.612; P < 0.05.

Table 15
Analysis of variance.

Model	Sum of squares	Df	Mean square	F	Sig.
Regression	177.981	4	44.495	10.612	.000
Residual	83.859	20	4.193
Total	261.840	24

Table 16 shows 68% explained variation found in the dependent factor of the knowledge level of the respondents regarding water-saving techniques after receiving training, which reveals that training of farmers at their farms through the implementation of learning models have a good impact on the enhancement of small scale farmers’ income and their competencies regarding on-farm irrigational management in the study area of District Sargodha.

Table 16
Model Summary.

Model	R	R Square	Adjusted R Square	Std. Error of Estimate
1	.824	.680	.616	2.04767

Table 17 explains the individual coefficient analysis in the regression model. Each predictor’s P values show that the respondents’ knowledge level regarding learning models, irrigation methods, and different measurement tools were strong predictors. It is concluded that farmers’ capacity building through training at their farms is the main challenge for agricultural extension research and field institutions.

Table 17
Model Regression Coefficient.

Model	-----------Unstandardized Coefficients----------		Standardized Coefficients	t	Sig
	B	Std. Error	Beta
(Constant)	29.455	1.731		17.020	.000
Learning Models	-2.255	.661	-2.505	-3.414	.003
Methods of Irrigation	.594	.175	.749	3.402	.003
Water-Saving Tech.	.204	.098	.478	2.078	.051
Tools	2.357	.802	2.029	2.940	.008

• a): Dependent Variable: Knowledge Level of the respondents regarding water-saving technique after receiving training.
• b): Predictors: (Constant),Knowledge level of the respondents before receiving training regarding; ¹Learning Models, ²Methods of Irrigation,³ Water Saving techniques and ⁴ Water, Salt and nutrient measurement Tools.

CONCLUSION

The study results concluded that farmers were keen enough to improve their on-farm water management competencies and expect a good impact on their livelihood regarding implementing the learning models under the project “Developing approaches to enhance farmer water management skills in Punjab, Sindh and Baluchistan in Pakistan”. There is a dire need to invest in expert knowledge and training opportunities to bring change in the farmers’ skills level regarding water management competencies through learning models. A similar conclusion was drawn by (ASHRAF et al., 2012) in their study while assessing in-service educational needs of Agricultural Officers for adaptation of remote sensing technology to implement precision agricultural practices in Baluchistan, Pakistan. Their study also concluded that training is the way forward for respondents to get desirable new knowledge and become more skillful to play their role in developing the community. The following are few recommendations for policymakers and project activities about the agriculture sector with significant importance of farmers’ participation in implementing learning models.

Training should pursue as like previous manners with the assurance of participatory approaches to attain farmers’ active participation. Meanwhile, it is recommended that there is a need to simplify steps during VM and CPWS models workshops to eliminate those aspects more culturally and contextually suitable. Examples include the De Bono Six Hat Technique, divergent and convergent phases, virtual academy. It is understood such revisions have taken place, and the new Farmer Integrated Learning Model (FILM) has been developed and are being trialed).

There is a dire need to set demonstration blocks in the study area regarding modern agricultural and irrigational management practices to urge society. However, farmers can see how new emerging agricultural practices and water-saving techniques help enhance small scale farmers’ income with efficient utilization of on-farm existing resources.

Education is a very prime indicator that influences the decision making of the farmers. Thus, the introduction of short courses for the farmers’ capacity building are the main extension challenge to bridge the gap of technical knowledge and technology transfer among farmers’ communities.

Educational, research, and public sector institutions must develop the agriculture sector by training farmers at their farms. A thematic focus is required to develop a collaborative approach to the efficient utilization of on-farm existing resources from even a small piece of land in the small scale farming community.

The financial issue also influences the decision making of the respondents. While the government should have been providing subsidies to farmers, it should now encourage small landholders to take the initiative without facing financial problems by supporting such farmers and encouraging empowerment.

There is also a need to distribute the print media reports in the study area to enhance the farmers’ knowledge and reading skills. Farmers claimed that knowledge level could increase regarding new emerging practices by providing print media.

The current study has limitations that we did not study the role of provincial water policy on farmer’s trainings and how training models could help to deal with future water policies. Further, we did not include farmer’s preferences regarding to pay for improved water services in this study. It is further recommended that in future studies the relationship of local knowledge of farmers and learning models must also be studied for managing water resources.

ACKNOWLEDGMENTS

Australian Center for International Agriculture Research (ACIAR) funded this research. Thanks go to the international and national research institutions of ACIAR, UC, NARC-SSRI, PCRWR, and SOFT for offering a scholarship to complete this research work as a student of the University of Sargodha, Punjab-Pakistan. The efforts rendered by Sandra Heaney Mustafa to improve socio-economic status of farmers and their irrigational management competencies are much appreciated.

REFERENCES

• AFIFAH, S. et al. How to calculate paired sample t-test using SPSS software: From step-by-step processing for users to the practical examples in the analysis of the effect of application anti-fire bamboo teaching materials on student learning outcomes. Indonesian Journal of Teaching in Science, v.2, n.1, p.81-92, 2022. Available from: <https://ejournal.upi.edu/index.php/ijotis/article/view/45895>. Accessed: Sept. 13, 2024.
  » https://doi.org/https://ejournal.upi.edu/index.php/ijotis/article/view/45895
• ASHRAF, E. et al. In-service educational needs of agricultural officers for adaptation of remote sensing technology for precision agriculture in Balochistan-Pakistan. Sarhad Journal of Agriculture, v.28, n.2, p.353-360, 2012. Available from: <Available from: https://go.gale.com/ps/i.do?id=GALE%7CA296764514&sid=googleScholar&v=2.1⁢=r&linkaccess=abs&issn=10164383&p=AONE&sw=w&userGroupName=anon%7E7d99b619&aty=open-web-entry >. Accessed: Nov. 13, 2023.
  » https://go.gale.com/ps/i.do?id=GALE%7CA296764514&sid=googleScholar&v=2.1⁢=r&linkaccess=abs&issn=10164383&p=AONE&sw=w&userGroupName=anon%7E7d99b619&aty=open-web-entry
• ASHRAF, E. et al. Knowledge level of farmers regarding producing, processing and marketing of dates in Panjgur-Balochistan, Pakistan. Sarhad Journal of Agriculture, v.34, n.2, p.225-493, 2018. Available from: <Available from: https://researcherslinks.com/current-issues/Knowledge-Level-of-Farmers-Regarding-Producing-Processing-and-Marketing-of-Dates-in-Panjgur-Balochistan-Pakistan/14/1/1405 >. Accessed: Nov. 13, 2023.
  » https://researcherslinks.com/current-issues/Knowledge-Level-of-Farmers-Regarding-Producing-Processing-and-Marketing-of-Dates-in-Panjgur-Balochistan-Pakistan/14/1/1405
• AZAD, A. et al. Pakistan Sindh water resources management -issues and options. Occasional Paper Series No. 15. FAO Investment Centre, 2003.
• BARMAN, S. et al. Impact of farmers’ training programme on scaling up of water productivity in agriculture-an analytical study in Assam. Indian Research Journal of Extension and Education, v.17, n.2, p.24-28, 2017. Available from: <Available from: https://www.researchgate.net/profile/SundarBarman/publication/316669558_Impact_of_Farmers’_Training_Programme_on_Scaling_up_of_Water_Productivity_in_Agriculture-An_Analytical_Study_in_Assam/links/590b4968a6fdcc49617ab0cc/Impact-of-Farmers-Training-Programme-on-Scaling-up-of-Water-Productivity-in-Agriculture-An-Analytical-Study-in-Assam.pdf >. Accessed: Sept. 28, 2024.
  » https://www.researchgate.net/profile/SundarBarman/publication/316669558_Impact_of_Farmers’_Training_Programme_on_Scaling_up_of_Water_Productivity_in_Agriculture-An_Analytical_Study_in_Assam/links/590b4968a6fdcc49617ab0cc/Impact-of-Farmers-Training-Programme-on-Scaling-up-of-Water-Productivity-in-Agriculture-An-Analytical-Study-in-Assam.pdf
• BRUNER, J. et al. The act of discovery. Harvard Educational Review, v.31, n.1, p.21-32, 1961. Available from: <Available from: https://psycnet.apa.org/record/1962-00777-001 >. Accessed: Nov. 13, 2023.
  » https://psycnet.apa.org/record/1962-00777-001
• DANIEL, J. Sampling essentials: practical guidelines for making sampling choices. Sage Publications, London, p.103, 2011. Available from: <Available from: https://doi.org/10.4135/9781452272047 >. Accessed: Nov. 13, 2023. doi: 10.4135/9781452272047.
  » https://doi.org/10.4135/9781452272047.» https://doi.org/10.4135/9781452272047
• DEWEY, J. How we think, DigCat, Publishers, 2022. Available from: <Available from: https://books.google.com/books/about/How_We_Think.html?id=0UnWmAEACAAJ >. Accessed: Nov. 13, 2023.
  » https://books.google.com/books/about/How_We_Think.html?id=0UnWmAEACAAJ
• El BILALI, A. et al. Groundwater quality forecasting using machine learning algorithms for irrigation purposes. Agricultural Water Management, v.245, n.3, p.106625, 2020. Available from: <Available from: https://doi.org/10.1016/j.agwat.2020.106625 >. Accessed: Aug. 29, 2024. doi: 10.1016/j.agwat.2020.106625.
  » https://doi.org/10.1016/j.agwat.2020.106625.» https://doi.org/10.1016/j.agwat.2020.106625
• FOFANA, B. et al. Innovative agricultural extension value chain-based models for smallholder African farmers. Frontiers of Agricultural Science and Engineering, v.7, n.4, p.418-426, 2020. Available from: <Available from: https://doi.org/10.15302/J-FASE-2020358 >. Accessed: Aug. 31, 2024. doi: 10.15302/J-FASE-2020358.
  » https://doi.org/10.15302/J-FASE-2020358.» https://doi.org/10.15302/J-FASE-2020358
• FREIRE, P. Pedagogy of the oppressed, Continuum Publishing Company, New York, 1984. Available from: <Available from: https://www.abebooks.com/Pedagogy-Oppressed-Freire-Paulo-Continuum-Publishing/31318257642/bd >. Accessed: Nov. 13, 2023.
  » https://www.abebooks.com/Pedagogy-Oppressed-Freire-Paulo-Continuum-Publishing/31318257642/bd
• ISON, R. el al. Challenges to science and society in the sustainable management and use of water: investigating the role of social learning. Environmental Science & Policy, v.10, n.6, p.499-511, 2007. Available from: <Available from: https://doi.org/10.1016/j.envsci.2007.02.008 >. Accessed: Nov. 13, 2023. doi: 10.1016/j.envsci.2007.02.008.
  » https://doi.org/10.1016/j.envsci.2007.02.008.» https://doi.org/10.1016/j.envsci.2007.02.008
• KIPTOT, E.; FRANZEL, S. Developing sustainable farmer-to-farmer extension: experiences from the volunteer farmer-trainer approach in Kenya. International Journal of Agricultural Sustainability, v.17, n.6, p.401-412, 2019. Available from:<Available from:https://doi.org/10.1080/14735903.2019.1679576 >. Accessed: Sept. 10, 2024. doi: 10.1080/14735903.2019.1679576.
  » https://doi.org/10.1080/14735903.2019.1679576.» https://doi.org/10.1080/14735903.2019.1679576
• NIKOLAOU, G. et al. Implementing sustainable irrigation in water-scarce regions under the impact of climate change. Agronomy, v.10, n.8, p.1120, 2020. Available from: <Available from: https://doi.org/10.3390/agronomy10081120 >. Accessed: Sept. 20, 2024. doi: 10.3390/agronomy10081120.
  » https://doi.org/10.3390/agronomy10081120.» https://doi.org/10.3390/agronomy10081120
• OSTERWALDER, A. et al. Value proposition design: how to create products and services customers want. John Wiley & Sons, New York, 2014. Available from: <Available from: https://www.wiley.com/en-us/Value+Proposition+Design%3A+How+to+Create+Products+and+Services+ Customers+Want-p-9781118968055 >. Accessed: Nov. 13, 2023.
  » https://www.wiley.com/en-us/Value+Proposition+Design%3A+How+to+Create+Products+and+Services+ Customers+Want-p-9781118968055
• PEREIRA, L. S. et al. Irrigation management under water scarcity.Agricultural Water Management, v.57, p.175-206, 2002. Available from: <Available from: https://doi.org/10.1016/S0378-3774(02)00075-6 >. Accessed: Sept. 28, 2024. doi: 10.1016/S0378-3774(02)00075-6.
  » https://doi.org/10.1016/S0378-3774(02)00075-6.» https://doi.org/10.1016/S0378-3774(02)00075-6
• QURESHI, A. S. el al. Water management in the Indus Basin in Pakistan: challenges and opportunities. Mountain Research and Development, v.31, n.3, p.252-260, 2011. Available from: <Available from: https://iwaponline.com/wp/article/23/6/1329/84494/Water-management-in-Pakistan-s-Indus-Basin >. Accessed: Nov. 13, 2023.
  » https://iwaponline.com/wp/article/23/6/1329/84494/Water-management-in-Pakistan-s-Indus-Basin
• RASCH, D. et al. The two-sample test: pre-testing its assumptions does not pay off. Stat Papers, v.52, p.219-231, 2011. Available from: <Available from: https://doi.org/10.1007/s00362-009-0224-x >. Accessed: Sept. 11, 2024. doi: 10.1007/s00362-009-0224-x.
  » https://doi.org/10.1007/s00362-009-0224-x.» https://doi.org/10.1007/s00362-009-0224-x
• REHMAN, F. et al. Drinking and irrigation quality of groundwater and health risk assessment due to arsenic exposure in Sheikhupura district, Punjab, Pakistan. Kuwait Journal of Science, v.50, p.368-375, 2023. Available from: <Available from: https://www.researchgate.net/publication/371060453_Drinking_and_irrigation_quality_of_groundwater_and_health_risk_assessment_due_to_arsenic_exposure_in_Sheikhupura_district_Punjab_Pakistan >. Accessed: Nov. 13, 2023.
  » https://www.researchgate.net/publication/371060453_Drinking_and_irrigation_quality_of_groundwater_and_health_risk_assessment_due_to_arsenic_exposure_in_Sheikhupura_district_Punjab_Pakistan
• RIST, S. et al. “It was hard to come to mutual understanding…”the multidimensionality of social learning processes concerned with sustainable natural resource use in India, Africa and Latin America. Systemic Practice and Action Research, v.19, n.3, p.219-237, 2006. Available from: <Available from: https://doi.org/10.1007/s11213-006-9014-8 >. Accessed: Nov. 13, 2023. doi: 10.1007/s11213-006-9014-8.
  » https://doi.org/10.1007/s11213-006-9014-8.» https://doi.org/10.1007/s11213-006-9014-8
• ROSEGRANT, M. W. et al. Global water demand and supply projections: part 2. Results and prospects to 2025. International Food Policy Research, v.27, n.2, p.170-182, 2002. Available from: <Available from: https://www.tandfonline.com/doi/abs/10.1080/02508060208686990 >. Accessed: Nov. 13, 2023.
  » https://www.tandfonline.com/doi/abs/10.1080/02508060208686990
• SAIDOV, J. D. et al. Problems of competency approach in developing students’ creativity qualities for creating a database. The Journal of Higher Education Theory and Practice, v.23, n.1, p.164-171, 2023. Available from: <Available from: https://articlegateway.com/index.php/JHETP/article/view/5786 >. Accessed: Nov. 13, 2023.
  » https://articlegateway.com/index.php/JHETP/article/view/5786
• SCHUSLER, T. M. et al. Social learning for collaborative natural resource management. Society & Natural Resources, v.16, n.4, p.309-326, 2003. Available from: <Available from: https://www.tandfonline.com/doi/abs/10.1080/08941920390178874 >. Accessed: Nov. 13, 2023. doi: 10.1080/08941920390178874.
  » https://doi.org/10.1080/08941920390178874.» https://www.tandfonline.com/doi/abs/10.1080/08941920390178874
• SHAH, N. A. et al. Assessing perceptions of the households-heads regarding food security status in drought-hit areas of district Tharparkar, Sindh, Pakistan: acase study for agricultural extension. Sarhad Journal of Agriculture, v.36, n.4, p.1162-1173, 2020. Available from: <Available from: https://doi:10.17582/journal.sja/2020/36.4.1162.1173 >. Accessed: Sept. 1, 2024. doi: 10.17582/journal.sja/2020/36.4.1162.1173.
  » https://doi.org/10.17582/journal.sja/2020/36.4.1162.1173.» https://doi:10.17582/journal.sja/2020/36.4.1162.1173
• SHAHID, M. A. et al. Assessment of water productivity enhancement and sustainability potential of different resource conservation technologies: a review in the context of Pakistan. Agriculture, v.12, n.7, p.1058-1074, 2022. Available from: <Available from: https://www.mdpi.com/2077-0472/12/7/1058 >. Accessed: Nov. 13, 2023.
  » https://www.mdpi.com/2077-0472/12/7/1058
• SPRIGGS, J.; CHAMBERS, B. Organic research and collaborative development (ORCD) of horticultural supply chains in the Asia-Pacific. Stewart Postharvest Review, v.7, n.2, p.1-9, 2011. Available from: <Available from: https://www.researchgate.net/publication/274927732_Organic_research_and_collaborative_development_ORCD_of_horticultural_supply_chains_in_the_Asia-Pacific >. Accessed: Nov. 13, 2023.
  » https://www.researchgate.net/publication/274927732_Organic_research_and_collaborative_development_ORCD_of_horticultural_supply_chains_in_the_Asia-Pacific
• VIRNODKAR, S. S. et al. Remote sensing and machine learning for crop water stress determination in various crops: a critical review. Precision Agriculture, v.21, p1121-1155, 2020. Available from: <Available from: https://doi.org/10.1007/s11119-020-09711-9 >. Accessed: Sept. 21, 2024. doi: 10.1007/s11119-020-09711-9.
  » https://doi.org/10.1007/s11119-020-09711-9.» https://doi.org/10.1007/s11119-020-09711-9
• WADDINGTON, H. et al. Farmer field schools for improving farming practices and farmer outcomes: a systematic review. Campbell Systemic Review, v.10, n.1, p.135, 2014. Available from: <Available from: https://onlinelibrary.wiley.com/doi/full/10.4073/CSR.2014.6 >. Accessed: Nov. 13, 2023. doi: 10.4073/CSR.2014.6.
  » https://doi.org/10.4073/CSR.2014.6.» https://onlinelibrary.wiley.com/doi/full/10.4073/CSR.2014.6
• XIULING, D. et al. The Impact of technical training on farmers adopting water-saving irrigation technology: an empirical evidence from China. Agriculture, v.13, n.5, p.956, 2023. Available from: <Available from: https://doi.org/10.3390/agriculture13050956 >. Accessed: Sept. 29, 2024. doi: 10.3390/agriculture13050956.
  » https://doi.org/10.3390/agriculture13050956.» https://doi.org/10.3390/agriculture13050956
• XU, E. et al. The effectiveness of collaborative problem solving in promoting students’ critical thinking: A meta-analysis based on empirical literature. Humanities and Social Sciences Communications, v.10, n.16, p.1-11, 2023. Available from: <Available from: https://doi.org/10.1057/s41599-023-01508-1 >. Accessed: Aug. 31, 2024. doi: 10.1057/s41599-023-01508-1.
  » https://doi.org/10.1057/s41599-023-01508-1.» https://doi.org/10.1057/s41599-023-01508-1

• 0
  CR-2023-0605.R1
• DATA AVAILABILITY
  All data generated or analysed during this study are included in this published article
• DECLARATION OF USE OF ARTIFICIAL INTELLIGENCE
  Artificial intelligence tools were not used during the write up of this research article.

Edited by

• Editors
  Leandro Souza da Silva (0000-0002-1636-6643) Raquel Breitenbach (0000-0002-9431-3766)

Data availability

All data generated or analysed during this study are included in this published article

Publication Dates

• Publication in this collection
  17 Oct 2025
• Date of issue
  2025

History

• Received
  13 Nov 2023
• Accepted
  24 Apr 2025
• Reviewed
  29 July 2025