Research article · 2024 · Full text
International Journal of Agriculture Extension and Social DevelopmentInvestigating the knowledge levels of trained farmers in natural farming: A study of Tonk district of Rajasthan
Volume 7(3) · Pages 594–597 · DOI: 10.33545/26180723.2024.v7.i3g.478 ↗
Research areasAgricultural Economics and PracticesLivestock Management and Performance ImprovementAgricultural Systems and Practices
Abstract
Author-writtenNatural farming is a type of farming that is pain-free, care-free, loan-free, and driven by passion, as described by Palekar, 2010. The study on natural farming in Tonk district, Rajasthan, included 120 trained farmers from three blocks, with an emphasis on pain-free, care-free, loan-free farming motivated by passion. Villages were selected based on preliminary surveys indicating a significant interest in ecological farming. A random sample of responders gave 74.17% with a medium degree of knowledge, with the Jeevamart and Beejamarita components demonstrating the highest understanding. Triangulation maintained validity and dependability, as evidenced by a Cronbach's Alpha of 0.641. According to the Durbin-Watson model, correlation and regression analyses revealed positive relationships between various factors such as education level, land ownership, experience in natural farming, mass media exposure, age, annual income, extension contacts, scientific orientation, and risk orientation
Summary
Source-checkedPlain-language reading of the full paper, prepared with AI and checked against the text. Each point links to the authors' own words.
Why it matters
- Zero budget Natural farming (ZBNF) is described as a self-sustaining, symbiotic approach to agriculture that aims to be painless, carefree, and loan-free. View evidence ↗
- The ZBNF methodology is intended to reduce the need for borrowing, which is linked to a decrease in farmer suicides. View evidence ↗
- The Rajasthan government initiated a pilot natural farming project called 'Kheti Mein Jaan toh Sashakt Kisan' in 2019-20 across the districts of Tonk, Sirohi, and Banswada. View evidence ↗
The question
- The study aimed to assess the awareness and understanding of natural farming concepts among farmers who had received training. View evidence ↗
What they did
- The researchers sampled 120 respondents from three blocks in the Tonk district of Rajasthan (Uniara, Niwai, and Deoli) that had the highest number of trained natural farming farmers. View evidence ↗
- To ensure reliability and triangulation, a separate group of 30 farmers from a non-sample area (Baori hamlet in Todaraisingh block) was given 60 statements. View evidence ↗
- Data analysis was performed using MS Excel 2011 and IBM SPSS 26, employing frequency, percentage, mean, standard deviation, correlation, and the Durbin-Watson regression model. View evidence ↗
What they found
- The majority of trained farmers (74.17%) had a medium level of knowledge, while 13.33% had a high level and 12.50% had a low level. View evidence ↗
- Among specific components, Whapasa (45.83%) and Soil Mulching (40.83%) had the highest percentage of farmers with a high level of understanding. View evidence ↗
- Jeevamart and Beejamarita were most commonly understood at a medium level, with 64.17% and 70% of farmers respectively in this category. View evidence ↗
- Agniastra and Neemastra were found to have a lower overall knowledge level compared to other natural farming components. View evidence ↗
- Knowledge levels were strongly and positively correlated with education (r=0.258), land holding (r=0.319), experience in natural farming (r=0.540), and mass media exposure (r=0.256). View evidence ↗
- Age, annual income, extension contacts, scientific orientation, and risk orientation showed weaker positive correlations with the farmers' knowledge levels. View evidence ↗
What it means
- The authors recommend focusing on improving education, facilitating land ownership, promoting experience, and utilizing mass media to encourage the adoption of natural farming. View evidence ↗
Visual summary
2 visualsIllustrations explain the paper; charts are replotted from the paper's own table values.
Percentage (%): Low (up to 24.97) vs Medium (24.98 to 35.1) vs High (>35.1)
Replotted from Table 3 (page 2) of the original paper. Every bar is the paper's own table value.
View source evidence ↗
Beejamarita and Soil mulching and more: 17.50% vs 64.17% vs 18.33%
Replotted from Table 4 (page 2) of the original paper. Every bar is the paper's own table value.
View source evidence ↗Figures & tables
From the original paper
Table 1
Table 2
Table 3
Table 4
Table 6
Table 5
Table 7
Table 8Introduction
Zero budget Natural farming is self-sustaining and symbiotic in nature Palekar (2014) [8] states (Khangarot et al., 2022) [3] . It is painless, carefree, loan-free, and passionless farming (Palekar, 2010; Münster, 2018) [9,6] . According to Padam Shree Shubhash Palekar, this methodology significantly decreases the incentive to borrow, resulting in a drop in farmer suicides across the country. Mr Palekar spent six years on his property, 1989-1995, studying natural systems and verifying natural forest processes. He discovered that around 98-98.5 percent of nutrients are derived from air, water, and sun radiation. The remaining 1.5 percent of nutrients are absorbed from the earth. Mr Palekar has supplied four components: Bijamrita (seed treatment with cow dung and urine), Jeevamrita (catalytic agent that promotes microorganism activity in soil), Mulching (to create a favorable microclimate in the soil), and Waaphasa (soil aeration). According to the economic study, around 1.6 lakh farmers use ZBNF in over 1000 communities with some sort of state assistance (Ministry of Agriculture and Farmers Welfare, 2021) [4] . The Rajasthan government launched a natural farming project in 2019-20. Kheti Mein Jaan toh Sashakt Kisan. Tonk, Sirohi, and Banswada are the three districts in the State where the effort, in the form of a pilot project, originally began. Under the initiative, master-trainers from the department led a two-day session that educated 18,313 farmers. There have been no research on natural farming in Rajasthan as of yet. As a result, assessing farmers' understanding of natural farming has arisen as an important issue worth investigating. As a result, this study endeavour was conducted to assess trained farmers' awareness and grasp of natural farming concepts.
Materials and Methods
The ex-post facto research design was adopted in this study. Tonk area of Rajasthan has been designated as the location of the current research enterprise. A total of 120 respondents were picked from Tonk district's three blocks. Tonk district in Rajasthan consists of seven blocks. Out of these, three blocks (Uniara, Niwai, Deoli) having highest trained natural farming farmers were selected based on data given by District Agriculture Officer, Tonk district. Two villages from each block with a total of six villages were selected randomly based on the assumption that these villages would possess highest number of trained farmers about natural farming. Assumptions for creating a list of villages were based on a pilot survey of the study area done by the researcher. Twenty farmers were randomly picked from each pre-determined hamlet. As a result, the total sample size for the study was 120 respondents. A standardized knowledge exam was devised to assess farmers' understanding of organic farming, taking into account the processes used by Sulaiman (1989) [12] , Bonny (1991) [1] , and Sushama (1993) [13] . However, the knowledge index was calculated by the formulae. www.extensionjournal.com To assess farmers' knowledge level, they were asked to answer several questions concerning the idea of natural farming. The respondents were divided into three categories: those 'with knowledge' and those with 'no knowledge', and were awarded a score of 1 or 0. The marks received under various questions were added up. Respondents were classified into three categories based on their overall score: low, medium, and high level of expertise. The activity-specific knowledge percentage was also determined. To calculate scale reliability and triangulation, a set of 60 statements was delivered to a new group of 30 farmers on a three-point continuum (actual sample size of n=120) from another section of the nonsample area. Todaraisingh block in Rajasthan's Tonk district was chosen at random, along with one hamlet named Baori. Thus, 30 farmers from one designated hamlet were chosen at random for reliability testing.
The Spearman (1910) and Brown (1910) formulas were employed to quantify reliability, together with Cronbach's alpha (α) (Cronbach, 1951).
Where, rhh = Pearson correlation between (odd and even)
Where, K=No. of items in the scale σ2yi= the variance of item i for the current sample of respondents σ2x= the variance of the scale The coefficient of correlation between forms (odd and even items) was 0.48 and Spearman-Brown Coefficient for both equal length and unequal length were 0.55, thus showing high reliability of the knowledge test.
Reliability score for knowledge test
Reliability score for knowledge test
Results and Discussion
Overall, Knowledge level of trained farmers about Natural farming Practices (N=120) The findings derived from the Table 3 indicate that a significant portion of farmers (74.17%) possess a moderate level of knowledge, while a relatively small number of farmers fall within the high (13.33%) and low (12.50%) knowledge level categories. These observations find validation and alignment within the existing research. Roy et al. (2007) [11] , Jakkawad et al. (2017) [2] , and Patel et al.
| Category | Frequency (f) | Percentage (%) |
|---|---|---|
| Low (up to 24.97) | 15 | 12.50% |
| Medium (24.98 to 35.1) | 89 | 74.17% |
| High (>35.1) | 16 | 13.33% |
Table text extracted from the original PDF; superscript letters are shown inline. The original table image is below.
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(2011) [10] has independently reported similar findings. Therefore, the current study's outcomes are substantiated by these prior research endeavours. The correlation analysis between the knowledge level of natural farming practices and various characteristics revealed significant factors influencing adoption. Strong positive correlations were found with education (r = 0.258**), land holding (r = 0.319**), experience in natural farming (r = 0.540**), and mass media exposure (r = 0.256**), indicating that higher education, larger land holdings, experience, and media exposure contribute to greater impact on knowledge level. Conversely, caste, family type, family size, and housing type showed weaker negative correlations, suggesting limited impact on knowledge levels. Other characteristics, such as age (r = 0.215*), annual income (r = 0.189*), extension contacts (r = 0.184*), scientific orientation (r = 0.218*), and risk orientation (r = 0.198*), demonstrated relatively weaker positive correlations with knowledge. The correlation coefficient (R) stands at 0.691, suggesting a moderately strong positive relationship between the predictors and knowledge level. About 47.70% of the knowledge variance is explained by the predictors, as denoted by the coefficient of determination (R Square). The adjusted R Square, slightly lower at 0.429, accounts for predictor penalties. The standard error of the estimate stands at 4.006, representing the average deviation between actual and predicted knowledge values. A Durbin-Watson value of 1.818 signifies an absence of significant autocorrelation in the model, confirming its reliability. To encourage natural farming, it would help to focus on better education, helping people own land, promoting experience, and using media to spread the word. These efforts can make farming eco-friendlier and stronger.
Component wise knowledge level of trained farmers about Natural farming
Component wise knowledge level of trained farmers about Natural farming
Regression analysis between knowledge level of respondents and independent variables
Regression analysis between knowledge level of respondents and independent variables
Figures and tables from the paper
| Case Processing Summary | — | n | % |
|---|---|---|---|
| Cases (Responses from non-sample farmers) | Valid Responses | 60 | 100.0 |
| Excludeda Responses | 0 | 000.0 | |
| Total | 60 | 100.0 | |
| a. List- wise deletion based on all variables in the procedure; n = no. of respondents |
Table text extracted from the original PDF; superscript letters are shown inline. The original table image is below.
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| Reliability Statistics | — | — | — |
|---|---|---|---|
| Cronbach's Alpha | Part 1 | Value | .481 |
| N of Items | 30a | ||
| Part 2 | Value | .528 | |
| N of Items | 30b | ||
| Total N of Items | 60 | ||
| Correlation Between Forms | .381 | ||
| Spearman-Brown Coefficient | Equal Length | .552 | |
| Unequal Length | .552 | ||
| Guttman Split-Half Coefficient | .550 | ||
| Cronbach's Alpha | .641 |
Table text extracted from the original PDF; superscript letters are shown inline. The original table image is below.
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| Knowledge Level | Major components of natural farming | — | — | — | — | — |
|---|---|---|---|---|---|---|
| Jeevamart | Beejamarita | Soil mulching | Whapasa | Agniastra | Neemastra | |
| Low Level | 17.50% | 12.50% | 12.50% | 34.16% | 20.00% | 58.33% |
| Medium Level | 64.17% | 70.00% | 46.66% | 20.00% | 48.33% | 8.33% |
| High Level | 18.33% | 17.50% | 40.83% | 45.83% | 31.67% | 33.33% |
Table text extracted from the original PDF; superscript letters are shown inline. The original table image is below.
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| S. No. | Characteristic | Correlation Coefficient (r) |
|---|---|---|
| 1. | Age | .215* |
| 2. | Education | .258** |
| 3. | Caste | -0.10 |
| 4. | Land holding | .319** |
| 5. | Area under natural farming | -0.081 |
| 6. | Annual income | .189* |
| 7. | Family type | -0.125 |
| 8. | Family Size | -0.029 |
| 9. | Housing type | -0.152 |
| 10. | Experience in natural farming | .540** |
| 11. | Extension contacts | .184* |
| 12. | Mass media exposure | 0.256** |
| 13. | Scientific orientation | .218* |
| 14. | Risk orientation | .198* |
Table text extracted from the original PDF; superscript letters are shown inline. The original table image is below.
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| ANOVAa | — | — | — | — | — | — |
|---|---|---|---|---|---|---|
| Model | Sum of Squares | df | Mean Square | F | Sig. | |
| 1 | Regression | 1596.272 | 10 | 159.627 | 9.945 | .000b |
| Residual | 1749.595 | 109 | 16.051 | |||
| Total | 3345.867 | 119 |
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| Independent Variables | Regression Coefficient | S.E. of Partial Regression Coefficient | t-value |
|---|---|---|---|
| Age | .008* | .035 | 2.710 |
| Education level | .003** | .474 | 3.013 |
| Caste | .027 | .518 | -2.246 |
| Land holding | .015** | .592 | 2.471 |
| Area of Natural Farming | .214 | .753 | -1.249 |
| Annual Income | .469 | .000 | .726 |
| family type | .325 | .831 | -.989 |
| Experience | .000* | .516 | 5.504 |
| Risk Orientation | .154 | .105 | 1.434 |
| Scientific Orientation | .083 | .138 | 1.749 |
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References
9 in the original paper- Bonny BP. Adoption of improved agricultural practices by commercial vegetable growers of Ollukkara blocks in Thrissur district. M.Sc., (Ag.) thesis (unpub.), Department of Agricultural Extension, Kerala Agricultural University, Thrissur; 1991.
- Jakkawad SR, Sawant RC, Pawar B. Knowledge and adoption of ginger production technology in Aurangabad district. Trends in Bioscience. 2017;10(24):5111-5114.
- Khangarot AK, Choudhary D, Singh K. Zero Budget Natural Farming (ZBNF)-Need of Sustainable Agriculture. AkiNik Publications, New Delhi; 2022. Pp. 21-33.
- Münster D. Performing alternative agriculture: Critique and recuperation in zero budget natural farming, South India. Journal of Political Ecology. 2018;25(1):748764. Stable URL.
- Niti Aayog. Natural Farming. Stable URL; 2022.
- Palekar S. The philosophy of spiritual farming: zero budget natural farming -part 1. 5th revised renewal ed. Amravati: Zero Budget Natural Farming Research, Development and Extension Movement; 2010.
- Roy S, Bhagat R, Rao DUM. Level of knowledge and extent of adoption of farmers on recommended gladiolus production practices. Research Journal of Extension Education. 2007;7(2&3):69-71.
- Sulaiman RV. Evaluative perception of appropriateness of the recommended fertilizer management practices. M.Sc., (Ag.) thesis (unpub.), Department of Agricultural Extension, Kerala Agricultural University, Thrissur; 1989.
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