Abstract
Bangladesh is a riverside country and Hakaluki haor is one of the major wetlands. This research aims to identify the key factors affecting Boro-rice production and also to discover the optimum farming system. Information was collected from 150 respondents through a structured questionnaire and also focus group discussion. Descriptive analysis and factor analysis was applied to identify the key factors of Boro-rice production. Among the problems of Boro-rice production, more than two-thirds of the farmer tackled irrigation-related problems whereas insect and disease problems affect all farmland. The price of inorganic fertilizer is high but two-third of them applies inorganic fertilizer. The rate of applying high yield variety (HYV) was found very low. A remarkable portion of the farmer practices mass media so information should be strengthening for increasing production. The rate of adoption method of modern technology (i.e., crop rotation and practice intercropping) is very poor. The agricultural extension service was not sufficient. More than half of the farmers sell their production in the crisis period. Flash flood was the major natural disaster and most of the farmers claimed that heavy rain and an unexpected misplaced dam was the prime reason for flashflood. In factor analysis, seed plant cost, plow cost, irrigation cost, as well as family size and cultivable lands (last year), were found as significant factors for Boro-rice production. So, the government may give more support to the farmers so that they increase cultivable land as well as sustain their livelihood.
Keywords: Hakaluki haor, Boro-rice production, Farming system, Crop rotation, Flashflood, Factor analysis.
Received: 16 September 2020 / Revised: 12 October 2020 / Accepted: 23 October 2020/ Published: 10 November 2020
Contribution/ Originality
This study is one of very few studies which have investigated to identify the key factors affecting Boro-rice production in Hakaluki haor and also discover the optimum farming system.
1. INTRODUCTION
Bangladesh is a riverside country whose backbone is agriculture. About 60-70% of the people of Bangladesh depend on agriculture which comprises about 15% of the country's GDP and 60% of the total labor force (Bishwajit, Razib, & Sharmistha, 2014). Bangladesh is located between the Himalayas and the Bay of Bengal very prone to natural disasters and one of the most climate-vulnerable countries in the world (World Bank, 2010). Climate change accelerated the intensity and frequency of occurrences of salinity, storms, drought, irregular rainfall, high temperature, flash floods, etc. that resulted from global warming. Global warming is harmful to crops of tropical countries (United Nations Environment Programme (UNEP), 2009). Agricultural production varies from region to region in our country due to different climatic and geological factors. In the country some of the areas are plain lands, some others are hilly lands, and some others are wetlands. Production practices and outcomes are different in these sectors. Wetlands in Bangladesh have long been facing serious degradation and loss, due to many natural and anthropogenic factors. Besides natural causes, factors like overuse of resources, lack of property rights, human encroachment and conversion to other uses, and also the absence of effective enforcement of laws are some of the most important factors for the decline in wetland biodiversity of the country (CWBMP (Coastal and Wetland Biodiversity Management Project), 2005). There are about 373 haor/wetlands located in the districts of Sunamganj, Habiganj, Netrakona, Kishoregan, Sylhet, Maulvibazar, and Brahmanbaria. These 373 haors cover an area of about 859,000 ha which is around 43% of the total area of the haor region (CEGIS, 2012). The total cultivated area of these districts is about 1.26 million hectares of which 0.68 million ha (nearly 66%) is under haor. Almost 80% of this area (i.e. 0.68 million ha) is covered by Boro rice, while only about 10% area is covered by T. Aman production (Huda, 2004). The wetlands provide the lion’s share of the boro rice production of our country which supplies the maximum portion of our main food.
Hakaluki haor is one of the major wetlands in greater Sylhet, as well as Bangladesh. With a land area of 18,386 hectares, it supports rich biodiversity and provides direct and indirect livelihood benefits to nearly 190,000 people (CWBMP (Coastal and Wetland Biodiversity Management Project), 2005). About 200,000 people live in the area surrounding Hakaluki haor (Rana, Sohel, Akhter, & Alam, 2009). The area was once known as the “Fishmine” of the country and due to the critical conditions of the haor ecology, the government of Bangladesh declared Hakaluki as an Ecologically Critical Area (ECA) in April 1999. Hakaluki haor is a complex ecosystem, containing more than 276 interconnecting Beels/Jalmahals (CWBMP (Coastal and Wetland Biodiversity Management Project), 2005). Hakaluki haor falls under the two administrative districts (Maulvibazar and Sylhet) and five Upazilas (Barlekha, Kulaura, Fenchuganj, Golapganj, and Juri). The two main sources of livelihood for these people are fisheries and agriculture. Haors and Floodplains in Bangladesh are important sources of fisheries resources for the country. Fish from the wetlands provide about 80 percent of the country’s dietary protein, and food, fuel, fiber, fodder, and building materials are also harvested from wetlands (SEMP, 2005). Several interacting factors such as agriculture, forestry, and fisheries all are sensitive to climate change. Land cover by human/natural modifications has largely resulted in deforestation, biodiversity loss, global warming, and an increase of natural disaster-flooding (Mas et al., 2004; Zhao, Lin, & Warner, 2004). Both human-induced and natural land cover changes can influence the global change because of its interaction with the terrestrial ecosystem (Houghton, 1994) biodiversity (Sala et al., 2000) and landscape ecology (Coughenour, Reid, & Thornton, 2000). These factors could result in more local responsibility for those people who are currently vulnerable or who could become so within the foreseeable future. Although short-term food security is achieved, for long-term food security complete supervision of the 1.99 million ha of wetlands is crying need to attain Millennium Development Goal (FAO, 2008). This document attempts to illustrate the factors affecting Boro-rice production in hoar areas and take the necessary steps to booster the economic activity of the country.
2. MATERIALS AND METHODS
This study was conducted on five Upazilas which encloses Hakaluki haor cover two administrative districts of Maulvibazar and Sylhet, Bangladesh from August 2017 to July 2018. A multi-stage cluster sampling was used for the selection of the respondents. Descriptive and multivariate statistical analysis was used for the identification of the key factors of Boro-rice production. Factor Analysis using Principal Component Analysis (PCA) was conducted to reduce the data and check the convergent strength of significant constructs. To find out the factors affecting Boro-rice production, a multiple regression was carried out with the Boro-rice yield as the dependent variable and the factors identified by the Factor Analysis using PCA as the independent variables.
The general expression of a regression model can be written as: y=f(x1,…,xn)+ε, in which: y-dependent variable (outcome variable) randomized; x1,…,xn independent variables (factorial) non-randomized; ε-random error variable. Random variable ε summarizes the influences of variables not included in the model that influences the variable y.
3. RESULTS AND DISCUSSION
3.1. Descriptive Analysis
The descriptive statistics Table 1 of different characteristics effects on Boro-rice production are as follows: Almost all respondents (89%) main occupation was agriculture. Generally, it is known that the high yield variety (HYV) gives comparatively more yield which confirms the two-third of the respondents of this study. Seventy-four percent of the farmer applied local paddy variety while 20.7% of farmers used high yield variety (HYV). Among the respondents, 70% used inorganic (chemical) fertilizer and only 6% used organic fertilizer where 24% used both of the fertilizer. Above one-third of the farmer said that the price of chemical fertilizer is not reasonable. In case of problems faced in Boro-rice production, irrigation related problems faced about 71% of the respondents, and all of the farmers faced by insect and disease-related problems. Crop rotation is also an important issue for the fertility of the land and the prevention of the insects. The crop rotation can able to earn income during the wet months when rice farming is not possible due to drowned farmland by water in the Hakaluki haor area. But the study results revealed that the farmer had no idea about the crop rotation, where only 3.3% were practiced crop rotation. It’s maybe reasons for the land shortage of different seasons. Practicing intercropping is a very well established cultivation technique and essential to meet the demand of the increased population of the country but the farmers of the Hakaloki haor only 3.3% practicing intercropping. Around half of the farmer’s households were found food insecure throughout the year. So these farmers need to take a loan from different sources to continue their farming and also meet up their basic needs. Around seventy-three percent of farmers of the Hakaloki haor area took loans for agricultural production and half of them mentioned that they have faced problems in taking the loan.
Different types of the problem faced by the farmer to take the loan for Boro-rice production among them they feel high-interest rate are the topmost problems (60%). Also, most of the farmers of the Hakaloki haor said the interest rate of the loan is not reasonable. The farmer must have the knowledge and practice of modern technology for increasing the yield which has a significant impact on production. The study revealed that the adaptation rate of modern technology is low (58%) of the Hakaloki haor basin farmer. In Bangladesh, agricultural production rapidly increases due to strengthening the extension service from the agricultural extension department. But only 46% of the farmer of this haor area said that they got the extension service from an agricultural extension officer. A large portion of the farmers (83.3%) was not satisfied with the visiting frequency of agricultural extension agents. A greater portion of the farmer (68%) mentioned that they practicing mass media for watching agricultural-related programs and weather information which is an important input in making farm decisions and almost all of them monitor the weather/climate regularly. The most useful climate information for them is on rainfall, temperature. As a climate indicator rainfall is very important for agricultural production. According to the farmers' perception, 82% of the farmers said that the rainfall increased, which has a positive sign for Boro-rice production although it harms loses the Boro-rice by the flash flood.
Table-1. Descriptive analysis of different factors affecting Boro-rice production.
| Characteristics | Frequency |
Percent |
Cumulative Percent |
| Occupation of the respondents | |||
| Agriculture | 133 |
88.7 |
88.7 |
| Fishery | 6 |
4.0 |
92.7 |
| Others | 11 |
7.3 |
100.0 |
| Practicing any mass media | |||
| Yes | 102 |
68.0 |
68.0 |
| No | 48 |
32.0 |
100.0 |
| Household food secure throughout the year | |||
| Yes | 78 |
52.0 |
52.0 |
| No | 72 |
48.0 |
100.0 |
| Irrigation related problems | |||
| Yes | 106 |
70.7 |
70.7 |
| No | 44 |
29.3 |
100.0 |
| Facing of insect and disease problems | |||
| Yes | 150 |
100.0 |
100.0 |
| No | 0 |
0 |
0 |
| Types of fertilizer use | |||
| Organic | 9 |
6.0 |
6.0 |
| Inorganic (chemical) | 105 |
70.0 |
76.0 |
| Both | 36 |
24.0 |
100.0 |
| Types of paddy variety applied | |||
| Local | 111 |
74.0 |
74.0 |
| High yield variety | 31 |
20.7 |
94.7 |
| Others | 8 |
5.3 |
98.7 |
| Perception about using HYV | |||
| Increased yield | 110 |
73.3 |
73.3 |
| Decreased yield | 10 |
6.7 |
80.0 |
| Constant yield | 30 |
20.0 |
100.0 |
| Ever use crop rotation | |||
| Yes | 5 |
3.3 |
3.3 |
| No | 145 |
96.7 |
100.0 |
| Practicing inter cropping | |||
| Yes | 5 |
3.3 |
3.3 |
| No | 145 |
96.7 |
100.0 |
| Get enough rain in the last production year | |||
| Yes | 56 |
37.3 |
37.3 |
| No | 94 |
62.7 |
100.0 |
| Activities out of farmland | |||
| Yes | 61 |
40.7 |
40.7 |
| No | 89 |
59.3 |
100.0 |
| Loan access for agricultural production | |||
| Yes | 109 |
72.7 |
72.7 |
| No | 41 |
27.3 |
100.0 |
| Adoption rate of modern technology | |||
| High | 2 |
1.3 |
1.3 |
| Average | 54 |
36.0 |
37.3 |
| Low | 87 |
58.0 |
95.3 |
| None | 7 |
4.7 |
100.0 |
| Extension service from agricultural extension officer | |||
| Yes | 69 |
46.0 |
46.0 |
| No | 81 |
54.0 |
100.0 |
| Local perception on Rainfall of Hakaloki haor | |||
| Increased | 123 |
82.0 |
82.0 |
| Decreased | 19 |
12.7 |
94.7 |
| Constant | 7 |
4.7 |
99.3 |
| Don`t know | 1 |
.7 |
100.0 |
Source: Field survey, 2018.
Figure 1 shows that the equipment related to increasing agricultural (Boro rice) production is not good enough for all of the farmers. Flashflood is the highest (24.7%) mentionable cause of food insecurity whereas 68.7% of farmers seemed that other causes are responsible for food insecurity Figure 2.
Sources of water for irrigation are a very important factor for Boro-rice production. In this case, around half (forty-seven percent) of the farmers depended on the river diversion and forty-one percent of the farmers were dependent on the private wells (hiring machine) for irrigation Figure 3. Out of farm activities is essential for improving the socio-economic conditions of a farmer. But Figure 4 indicates that a large number of the farmer (60%) did not participate in any off-farm activities. Only 16.7% were engaged in wage labor, 8% were in business 5.3% were in a job and 4% were in rajmistri.
The total number of earning persons and the number of dependent persons is a very crucial factor for agricultural production as well as the economic condition of a family. A large number of farmers (sixty-eight percent) said that their family had only one or two earning persons Figure 5. Figure 6 shows that more than 70% of the farmer of the Hakaluki haor basin area had four or greater dependent persons in their family.
3.2. Factor Analysis (PCA)
The initial step in factor analysis was to assess the suitability. The Mayer-Kaiser-Olkin (KMO) measure of sampling adequacy gives a value of 0.523 (>0.5), which suggests that correlations between pairs of variables can be explained by other variables and also, the null hypothesis that the correlation matrix is an identity matrix is rejected (p<0.1) by Bartlett’s Test of Sphericity. Therefore, factor analysis is appropriate for the data set.
Table 2 represents the initial and extracted value for every variable in the communality tables. This table of communalities shows the amount of common variation shared with the remaining variables after the desired numbers of factors have been extracted. According to this table, the variables comprise at least 50% of the variation in each of the variables, represented a highly acceptable result.
Table-2. Communalities of variables.
| Variables | Initial |
Extraction |
| Age of the respondent (years) | 1.000 |
0.751 |
| Years of schooling | 1.000 |
0.662 |
| Size of family (No) | 1.000 |
0.623 |
| Cultivable land last year in care(30 decimals) | 1.000 |
0.632 |
| Seed plant cost in care(30 decimals) | 1.000 |
0.560 |
| Plough cost in care(30 decimals) | 1.000 |
0.610 |
| Pesticides cost in care(30 decimals) | 1.000 |
0.703 |
| Fertilizer use kg in care(30 decimals) | 1.000 |
0.689 |
| Irrigation cost in care(30 decimals) | 1.000 |
0.408 |
Source: Field survey, 2018.
Table-3. Total variance explained.
| Component | Initial Eigenvalues |
Extraction Sums of Squared Loadings |
Rotation Sums of Squared Loadings |
||||||
Total |
% of Variance |
Cumulative % |
Total |
% of Variance |
Cumulative % |
Total |
% of Variance |
Cumulative % |
|
| 1 | 1.702 |
18.913 |
18.913 |
1.702 |
18.913 |
18.913 |
1.608 |
17.864 |
17.864 |
| 2 | 1.396 |
15.509 |
34.422 |
1.396 |
15.509 |
34.422 |
1.348 |
14.980 |
32.844 |
| 3 | 1.346 |
14.957 |
49.379 |
1.346 |
14.957 |
49.379 |
1.343 |
14.918 |
47.761 |
| 4 | 1.194 |
13.263 |
62.642 |
1.194 |
13.263 |
62.642 |
1.339 |
14.880 |
62.642 |
| 5 | 0.847 |
9.416 |
72.057 |
||||||
| 6 | 0.722 |
8.027 |
80.084 |
||||||
| 7 | 0.663 |
7.362 |
87.445 |
||||||
| 8 | 0.609 |
6.769 |
94.214 |
||||||
| 9 | 0.521 |
5.786 |
100.000 |
||||||
Note: Extraction Method: Principal Component Analysis.
Table 3 depicts the eigenvalues for each factor variable. The eigenvalue for a factor explains the power of variance. The eigenvalue for variable V1 (age of the respondent), V2 (Years of schooling), V3 (Size of the family) and V4 (Cultivable land last year) are 1.702, 1.396, 1.346, and 1.194, respectively which had 18.913, 15.509, 14.957 and 13.263 percent power of variance, respectively. Using this factor analysis, four factors were extracted, accounting for 62.64% of the total variation in the data. From the table, it can be observed that the first extracted factor accounts for a large part of the total variance explained in the data, followed by the other factors which account for lesser and lesser of the total variance. It also represented extracted variables i.e. the number of variables retained. The rotated sum of squared loadings was considered because the result from rotation is more reliable than without rotated solution in factor analysis.
Table-4. Factor component matrix.
Component |
||||
1 |
2 |
3 |
4 |
|
| Age of the respondent (years) | 0.595 |
|||
| Years of schooling | 0.539 |
|||
| Size of family (Number) | 0.585 |
|||
| Cultivable land last year in care(30 decimals) | 0.509 |
|||
| Seed plant cost in care(30 decimals) | 0.575 |
|||
| Plough cost in care(30 decimals) | 0.618 |
|||
| Pesticides cost in care(30 decimals) | 0.503 |
|||
| Fertilizer use kg in care(30 decimals) | 0.612 |
|||
| Irrigation cost in care(30 decimals) | 0.593 |
|||
Source: Field survey, 2018.
The factor component matrix is disclosed in Table 4. The matrix exhibited the relationship between the factors and individual relationships. To estimate a more suitable result, it was essential to establish a rotated factor matrix.
Table-5. Rotated factor matrix: Varimax with kaiser normalization.
Component |
||||
1 |
2 |
3 |
4 |
|
| Age of the respondent (years) | 0.846 |
|||
| Years of schooling | -0.740 |
|||
| Size of the family (Number) | 0.776 |
|||
| Cultivable land last year in care (30 decimals) | 0.743 |
|||
| Seed plant cost in care (30 decimals) | 0.708 |
|||
| Plough cost in care (30 decimals) | 0.725 |
|||
| Pesticides cost in care (30 decimals) | -0.792 |
|||
| Fertilizer use kg in care (30 decimals) | 0.795 |
|||
| Irrigation cost in care (30 decimals) | 0.623 |
|||
Source: Field survey, 2018.
From this rotated factor matrix Table 5, it would be easy and possible to interpret the extracted factors with percentage variations explained. Now, these 4 factors can be considered:
Factor 1 (F1): Factor 1 was regrouping into V5, V6, and V9 variable i.e. Seed plant cost in care (30 decimals), Plough cost in care (30 decimals), and Irrigation cost in care (30 decimals) respectively.
Factor 2 (F2): Factor 2 was regrouping into V3 and V4 variable i.e., Size of the family (Number) and Cultivable land last year in care (30 decimals) respectively.
Factor 3 (F3): Factor 3 was regrouping into V7 and V8 variable i.e. Pesticides cost in care (30 decimals) and Fertilizer use kg in care (30 decimals) respectively.
Factor 4 (F4): Factor 4 was regrouping into V1 and V2 variable i.e. Age of the respondent (years) and Years of schooling respectively.
3.3. Factors affecting on Boro-Rice Production
All extracted factors together explained 62.64% of the rice production. The remaining 27.36% was explained by some other intrinsic factors. The overall regression equation was also found to be significant (F= 4.25, p < 0.03) indicating that these factors together significantly influenced the production. Among the four factors accounting for yield, factor 1 and factor 2 was highly significant Table 6 which means that production of Boro-rice will be averagely increased by 0.938 unit and 1.30 unit with one unit increase of factor 1 ((i.e. Seed plant cost in care (30 decimals), Plough cost in care (30 decimals) and Irrigation cost in care (30 decimals)) and factor 2 ((i.e. Size of the family (Number family person) and Cultivable land last year in care (30 decimals)) if other things remaining same.
Table-6. The coefficients of Multiple Regression and their significance.
Parameters |
Coefficient |
t-value |
P-value |
Factor 1 |
0.938 |
2.342 |
0.021 |
Factor 2 |
1.301 |
3.248 |
0.001 |
Factor 3 |
0.336 |
0.838 |
0.403 |
Factor 4 |
0.206 |
0.514 |
0.608 |
4. CONCLUSION
The study was undertaken to determine the significant factors that affect the Boro-rice production of the Hakaluki Basin, Bangladesh. Farmers' view about weather information is an important effort in making farm decisions. The maximum portion of the farmers seemed that the climatic indicator rainfall is irregular but the sudden flash flood is a prime cause of food insecurity. Farmers who were vulnerable due to flash flood and their agricultural production and also fodder for livestock damage drastically. Seed plant cost, plow cost, irrigation cost, size of family, and cultivable land last year were found as the significant contributing factors to the Boro-rice production of the Hakaluki Basin. So, different government and non-government organizations can take part in an important role by assisting the farmers in these areas of weakness. Especially the department of Agricultural Extension may take steps to offer training and extension service to the farmers on the diverse aspects of Boro-rice production.
Funding: The authors are thankful to the University Grants Commission, Bangladesh, and SAURES, Sylhet Agricultural University for financial support rendered to this research work. |
Competing Interests: The authors declare that they have no competing interests. |
Acknowledgement: All authors contributed equally to the conception and design of the study. |
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