Determinants Influencing the Adoption of Rice Intensification System among Smallholders in Mwea Irrigation Scheme, Kenya
Determinants Influencing the Adoption of Rice Intensification System among Smallholders in Mwea...
Kadipo Kaloi, Francis;Isaboke, Hezron Nyarindo;Onyari, Charles Nyambane;Njeru, Lucy Karega
2021-03-16 00:00:00
Hindawi Advances in Agriculture Volume 2021, Article ID 1624334, 8 pages https://doi.org/10.1155/2021/1624334 Research Article Determinants Influencing the Adoption of Rice Intensification System among Smallholders in Mwea Irrigation Scheme, Kenya 1 1 2 Francis Kadipo Kaloi , Hezron Nyarindo Isaboke, Charles Nyambane Onyari, and Lucy Karega Njeru Department of Agricultural Economics and Extension, University of Embu, P.O Box 6, Embu 60100, Kenya Department of Land and Water Management, University of Embu, P.O Box 6, Embu 60100, Kenya Department of Agricultural Economics, University of Nairobi, P.O Box 29053-00625, Nairobi, Kenya Correspondence should be addressed to Francis Kadipo Kaloi; kadipo.francis@embuni.ac.ke Received 24 February 2020; Revised 6 January 2021; Accepted 3 March 2021; Published 16 March 2021 Academic Editor: Jiban Shrestha Copyright © 2021 Francis Kadipo Kaloi et al. *is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Rice farming has received considerable attention in developing countries and particularly in Kenya due to its impact on smallholders’ income and food security. Irrigated rice is the largest consumer of water, and its sustainability is threatened by water shortage. *is has necessitated the development of alternative irrigation water technologies, such as the system of rice inten- sification (SRI), which are efficient in water use with improved yields. *is study analyzed the determinants of adoption of SRI in the Mwea Irrigation Scheme where stratified sampling was used to obtain 364 smallholder rice farmers. A semistructured questionnaire was used to collect primary data, which was then analyzed using a binary logistic regression model. *e results showed that age (−0.3%) was significant but with a negative effect on adoption of SRI. Farm size (2.499%), household size (1.895%), distance from the canal (1.354%), off-farm work (3.953%), access to credit services (8.714%), access to extension services (7.809%), and years in rice farming (0.409%) were found positively and significantly influencing factors to the adoption of SRI. *erefore, this study concludes that smallholders attempt to improve rice productivity through adoption of SRI should give a special priority to all significant factors. Green Revolution involves the development of high-yielding 1. Introduction varieties of cereal grains and modernization of farmland Rice (Oryza sativa L.) is one of the most important food management techniques. *e innovation was very effective crops for more than 50% of the world population [1] and and successful in Asia whereby many farmers were able to significantly influences food security in most countries [2]. adopt the technology [9,10]. However, the Green Revolution About 160 million hectares are estimated to be under rice was not able to help many African countries due to limited production globally with an approximate annual production infrastructure and financial constraints [6]. *e system of rice of approximately 500 million metric tons [3]. *e demand intensification (SRI) was developed about 30 years ago and for irrigation water exceeds the amount of water available for has been reported to offer an opportunity for reducing water rice irrigation in Kenya [4]. *erefore, alternative practices use while maintaining high yields. According to [11], SRI is a that reduce water use need to be put in place to enhance concept on the manipulation of agronomic practices to attain sustainable rice production [5]. higher rice yields with the use of minimal resources such as Various methods have been used to reduce farm input use agrochemicals, seeds, and water (no continuous flooding in in rice production [6]. One of the most tried methods was the SRI as compared to traditional methods). SRI is gaining Green Revolution in Asia, which involved a series of research popularity in all rice-growing areas of the world and that and technology transfer initiatives [7]. According to [8], the farmers can grow more rice with less water input [7]. 2 Advances in Agriculture However, alternative production practices such as SRI have *e key components of SRI include water management which is practiced by keeping the soil well-drained rather not yet been fully investigated especially on adoption. Previous studies on SRI in the Mwea Irrigation Scheme than continuous flooding and saturated during the vege- tative growth period. *e SRI modifies farm practices for include studies of Ndiiri et al. [6,24]. Studies such as [6] managing water use, nutrients, and soils. *e two possi- focused on the constraints and the returns associated with bilities suggested for water management in SRI involve the SRI while the study [24] focused on the perceptions of SRI. application of a small quantity of water daily but leaving the From these studies, little has been done or investigated on fields dry for short periods (2–7 days) to the point of surface determinants of SRI in the Mwea Irrigation Scheme. *e cracking. *e other one is flooding and drying the fields for knowledge on SRI is still scanty especially on the application of econometric modeling. *erefore, this study provides a alternating periods of 3–6 days each [12]. *e second component is the planting method which involves spacing strong case of the argument of using SRI to generate in- formation on determinants of SRI adoption with a view of configurations and the age of seedlings. In SRI, seedlings are transplanted 8–15 days after germination [13]. Some studies driving policy recommendations and filling the information gap in Kenya. suggest a line spacing of 30 cm × 30 cm. *e spacing could be based on the local edaphic conditions but it has to facilitate weeding [14]. *e third component is weed control which is 2. Materials and Methods best done ten days after transplanting and then weeding 2.1. Study Area and Methods of Data Collection. *e study every ten days until canopy closure [13]. *e fourth com- was conducted in the Mwea Irrigation Scheme (MIS) in ponent is soil fertility management. Most farmers use Kirinyaga County, Kenya (Figure 1). *e scheme is located compost or organic manure but the amount applied varies in in the central part of the country. It occupies the lower terms of its availability and also because there is no fixed altitude zones of the region with expansive low marshy areas. recommended rate to follow [13]. *e altitude ranges from 1,000 to 2,200 m above the sea level, Most of the recent and previous studies have shown that ° ° with temperatures ranging between 15 C and 30 C. *e area farm characteristics, household characteristics, and insti- experiences bimodal types of rains with the short rains tutional factors have a significant influence on the adoption occurring from October to December and the long rains of farming technologies [15]. According to Danso-Abbeam occur between March and May. *e main agricultural ac- et al. [16], the age of the household size, level of experience, tivity is monocropping of rice grown in paddies that are farm workshop attendance, the number of years in formal irrigated for six months. *e main sources of water for the education, availability of labour, and extension contact in- scheme are the River Nyamindi and River *iba which are fluence the adoption of improved maize varieties. Similarly, tributaries of the River Tana. *ere are approximately 7320 Gershon et al. [17] reported that farmers who managed the households within the main scheme [25]. postharvest losses were young, had formal education, and A field survey was carried out using a semistructured had fewer household members. A study conducted by Anang questionnaire to get quantitative data from the smallholder and Yeboah [18] established that years of education, credit rice farmers. Additionally, key informant interviews were access farmer experience, and geographical location were the undertaken for the qualitative data. Well trained enumer- factors determining the income from the off-farm work. *e ators were employed to collect data during the study period. welfare impacts of SRI revealed that all combinations of SRI Following this, respondents were selected using a stratified individually and as a group (water management, plant random sampling technique. *is was done with the aid of management, and soil management) had a positive impact the rice units as strata. 12 units were randomly selected from on productivity as reported by [19]. Similarly, according to the 20 units which are within the 4 major rice-producing [20], farmer’s location income, interest rates, rice farming blocks in the irrigation scheme. *e major blocks include experience, and the distance to the source of credit are Karaba, Tebere, Wamumu, and *iba. A total of 30 statistically significant determinants of the amount of credit smallholder rice farmers were selected per unit, and about 91 received. per block were sampled to give a total of 364 respondents. According to Noltze (2012), SRI seemed to be adopted more on plots and by farmers with less than average yields. *e results also noted that SRI may not be beneficial when 2.2. 0eoretical Review. Smallholder farmer’s perception is compared to conventional flooding (CF) rice grown under to maximize on their perceived utility. *e study was based favorable conditions and with the best management prac- on the subjective expected utility framework. *e individual tices. *ere exists a threshold for the effect of education on expected utility of innovation can be approximated in the agricultural productivity change as reported by Fung-Mey following equations: Huang [21] and Myint and Napasintuw [22]. According to [23], households who used improved seed varieties tend to SEU(π) � p U(πi) (1) be different from those that do not. *ey also have a higher consumption expenditure. *e results indicate the potentials 1− RRA of the improved seed varieties in helping the households in (2) U(π) � , 1 − RRA especially in rural areas increasing their welfare. Many empirical studies have investigated the issue of where p is the probability of the state of nature i for the adoption (see, e.g., [23], Noltze (2012), and Varma [19]. profit (π ), RRA is the relative risk aversion coefficient, and i Advances in Agriculture 3 35°0′0″E 40°0′0″E Kenya 5°0′0″N 5°0′0″N 0°0′0″ 0°0′0″ Kilometers 5°0′0″S 5°0′0″S 0 105 210 420 630 Kilometers 35°0′0″E 40°0′0″E 0 165 330 660 990 1,320 37°0′0″E 37°10′0″E 37°20′0″E 37°30′0″E 37°40′0″E 0°10′0″S 0°10′0″S Meru Laikipia Tharaka 0°20′0″S 0°20′0″S Nyeri 0°30′0″S 0°30′0″S Kirinyaga Embu 0°40′0″S 0°40′0″S Murang’a Machakos Machakos 37°0′0″E 37°10′0″E 37°20′0″E 37°30′0″E 37°40′0″E Counties Kenya Figure 1: Map of the Mwea Irrigation Scheme in Kirinyaga County. SEU is the subjective expected utility. When farmers have a function of the observed explanatory variables, x and an choice, they do select the alternative with the highest utility error term ε : (equation (3)). Based on the random utility theory, the global y � x β + ε . (5) 1 i i utility of a system is composed of the utility of each char- acteristic of the cropping system. Although profit could be SRI adoption can be expressed by a binary model with one of the characteristics, farmers also maximize their utility two options: if yes, y � 1, and otherwise, y � 0; the probability based on other factors such as agronomic and technical: of y � 1 is expressed by a formula as indicated in equation (2): U > U , (3) k j P Y � 1lx � G x , β . (6) r i i i where G which is a function with two values, zero or one, can be U t , t , . . . , t + ε, (4) 1 2 r expressed as follows: where t , t . . . t corresponds to the r characteristics of Pr(Adopt � 1) � G β + β x + · · · β x + e. (7) 1 2 r 1 k o 1 k innovation while the error term (ε) depicts the individual Pr(Adopt � 1) determines the probability of adopting determinants. SRI by the smallholder farmer given the predictor variables x , . . . , x . *e β is the intercept, and β , . . ., β are the i k o 1 k estimated parameters for the predictor variables while e is 2.3. Empirical Model. To determine the selected determi- the error term: nants of SRI adoption, a binary logistics regression model was used. *e smallholders were classified as either as the exp(z) G(z) � . (8) adopters whose value was equal to 1 or the nonadopters 1 + exp(z) whose value was equal to 0 [26]. *e probability function for the farmers who choose to *e predictor variables were the gender of the household adopt SRI can be represented as a latent variable y , a head (X ), age of the smallholder farmer (X ), marital 1 1 2 4 Advances in Agriculture spent 8.1 years in rice farming. *e mean difference in the two status (X ), education level (X ), household size (X ), farm 3 4 5 size (X ), monthly income (X ), off-farm work (X ), years groups was significant indicating that adopters were less ex- 6 7 8 perienced in rice farming as compared to the nonadopters. *e spent in rice farming (X ), access to extension services (X ), access to credit (X ), and the distance from the canal results agree with the findings of [19], who reported that the 10 11 (X ). Table 1 provides the description and measurement of number of years spent on rice farming reduced the adoption of the predictor variables. new agricultural technologies. *is means that a majority of the *e Breusch–Pagan test was used to test the presence of nonadopters are accustomed to the old way of rice farming heteroscedasticity. *e test compared the alternative hy- while the adopters are willing to take up the challenge of a new pothesis and the null hypothesis. *e results showed that the and promising production technology. *e results further value of the chi-square statistics is less than 0.05. *erefore, showed that 92.66% of the adopters received extension services the null hypothesis was rejected at a 5% level of significance while 7.34% did not. For the nonadopters, 69.52% reported that they received extension services while 30.48% did not. *e (Table 2). results were significant indicating that most adopters receive extension services as compared to nonadopters. *e study 3. Results and Discussion asked the respondents to indicate whether they accessed credit services in their rice farms. *e results showed that 33.98% of 3.1. Characteristics of the Respondent according to Adoption Status. *e mean age of the respondent was 42 years with a the adopters received credit services while 66.02% reported that relatively high proportion of middle age rice farmers among they did not receive credit services. It was observed that 91.43% the respondents as shown in Table 3. *e results were of the nonadopters reported that they did not receive credit significant implying that young farmers were actively in- services while 8.57% did. *e results were significant implying volved in farm operations. Furthermore, the results showed that the majority of the smallholders did have access to credit that 56.33% of adopters had obtained primary education, within their locality. 79.17% obtained secondary level and 97.37% had achieved tertiary education. Among the nonadopters, 43.67% ob- 3.1.1. Determinants of Adoption of SRI. *e determinants of tained primary education, 20.83% achieved secondary ed- SRI adoption were analyzed using a binary logistic regression ucation, and 2.63% had tertiary education. *e results were significant implying that the majority of the adopters had model. *e smallholder farmers were classified as either adopters or nonadopters of the SRI technology compared to acquired formal education as compared to nonadopters. *is confirms the findings of [27]. *e results further conventional flooding (CF). *e likelihood ratio estimates in Table 4 show that all the chi-square statistics are significant at 1% showed that the mean household size for the adopters was 4.12 and 5.12 for the nonadopters. *e results were signif- (p< 0.001). *is shows that the binary logistics model was the most appropriate in explaining the determinants of SRI icant revealing that the nonadopters had relatively large- adoption. *e model accounted for 77.8% of the variation sized households than the nonadopters. Analysis of the between SRI and CF. 8 out of the 12 variables were highly occupation showed that 88.42% of the adopters were un- significant. dertaking casual work and 3.86% were livestock keepers, *e estimated coefficient for age had a negative effect on while 91.43% of the nonadopters were casual workers and the adoption of SRI. *is indicated that the adoption of SRI 75.57% were livestock keepers. *e results were significant showing that most of the nonadopters of SRI were under- decreased with the age of the farmer. *e results implied that a unit increase in the age of the farmer decreased the likelihood taking off-farm occupations. Furthermore, the study findings revealed that the av- of adopting SRI by 0.3%. Older farmers may be more con- servative, and they do not want to change their farming erage distance from the canal for the adopters of SRI was 5 km and for the nonadopters, and it was 4 km. *e results practices from CF to SRI while the younger counterparts preferred SRI due to their familiarity with the technology were significant, implying that the adopters were far from information. Furthermore, the results show that younger the water source as compared to nonadopters. *erefore, the farmers remain essential as the primary audience for the adopters needed to be efficient in water usage due to dif- adoption of new agricultural technologies such as SRI. *ese ficulty and cost of accessing water from the main canals. results agree with the findings of [28], Varma [29], and *e study assessed the farm size of the respondents. *e Chuchird et al. [30–32] who reported a negative relationship mean farm size for the SRI farmers was 1.5 Ha and 2.1 Ha for the nonadopters. *e findings were significant confirming between age and adoption of farming technologies. Household size was found to have a significant and a that the nonadopters had large holdings as compared to adopters of SRI. *e monthly income of the respondents was positive relationship with the adoption of SRI. *e findings show that family size influences the adoption of SRI posi- tabulated in Kenya shillings (KES). *e average monthly income of the adopters was KES 40,374.52 while the average tively and a unit increase in household size will increase the adoption of SRI by 4.5%. *is shows that SRI is labour monthly income for the nonadopters was KES 33,761.90. *e intensive and therefore large families attract labour required results were significant. *is implied that SRI adopters had a in nursery preparation, land leveling, transplanting of young higher monthly income than the nonadopters. seedlings, and weeding. *e findings corroborate those of *e study assessed the years that farmers were involved in Kinuthia [23] who reported similar results in Uganda and rice farming. *e results showed that the adopters of SRI have Tanzania. spent 6.2 years in rice farming while the nonadopters have Advances in Agriculture 5 Table 1: Description of variables. Variable Description Measurement Age Age records the age of the farmer Number of years 1 for male Gender Gender is a variable that indexes the gender of the adopter 0 for female Household size Records the number of family members living in the same household Number of family members 1. Primary education Education level Households’ level of education 2. Secondary education 3. Postsecondary 1. Casual work Off-farm occupation *e variable measures whether the household has any other occupation 2. Livestock keeping 3. Others Farm size *is variable indexes households with farms under rice production Number of hectares (Ha) 1. Access extension service Access to extension services *is variable indexes trainings on SRI 0. Does not access Kenya shillings per Monthly income Measures the monthly income for the households household. (KES) 1. Access Access to credit Whether households access credit 0. Does not access Distance from the canal *is variables measures distance from the main canal in kilometers Distance in kilometers (KM) Authors’ source, 2020. innovative agricultural practices, thus a positive relationship Table 2: Testing for heteroscedasticity. between farmer experience and adoption of SRI technology. Breusch–Pagan/Cook–Weisberg test for heteroscedasticity *e results agree with the findings of [37] who reported that Ho: constant variance farmers endowed with knowledge and experience easily Variables: fitted values of SRI_Adoption understand or grasp the new technologies. chi2(1) � 4.38 Moreover, the econometric model results revealed that Prob> chi2 � 0.0364 access to extension services increases the adoption of SRI by Source: authors’ calculation, 2020. 94.5%. *e results imply that farmers who have access to ex- tension have a higher probability of adopting SRI since ex- Farm size was found to have a positive and significant tension services serve as an important source of information on effect on adoption of SRI. *is means that farm size increases agricultural production. Farmers who have significant extension the adoption of SRI by 5.9%. Farmers with large farms are contacts have better chances to be aware of various management practices that they can use to increase production. Similar results likely to experiment with new technologies on small fields before adopting in a large scale. *is observation agrees with were reported by [38], who found out that access to the ex- tension has a positive relationship with the adoption of farming studies by [33] and Ghimire et al. [23,26] who reported that owning more farmlands is correlated with the adoption of practices. Also, Ahmed et al. [1] indicated that access to ex- agricultural technologies. tension services positively affects the adoption of maize varieties. *e significant and positive results of off-farm work As it was hypothesized, access to credit services had a shows that participation in off-farm work increases the positive relationship with adoption of SRI. Access to credit adoption of SRI by 9.3%. *e results revealed that small- facilities increases the adoption of SRI by 20.6%. Credit services holders who are engaged in other off-farm activities are increases the purchasing power of agricultural inputs. *e likely to adopt SRI than those who concentrated entirely on results agree with the findings of [33] who reported that credit rice farming. Also the income received from off-farm ac- is an important determinant in the adoption of agricultural technologies. tivities was used to meet some of the farm operation cost in SRI farming. *is findings agree with the studies of [34,35] *e results further show that distance from the canal was significant at 5% with a positive coefficient. *erefore, an who found that off-farm income increased adoption of production technologies. Similarly, Kaloi et al. [36] found increase in the distance from the canal increased the that participation in off-farm activities positively affects the adoption of SRI by 3.2%. *is implies that as the distance adoption of agricultural technologies. *is may be due to increases, less water is available for the SRI farmers who then frequent access to information flow that is important in become more efficient in using their inputs such as water in understanding the new agricultural technologies. rice production. *is implied that those farmers who were Experience in paddy farming significantly increases the far away from the canals had higher adoption status than adoption of SRI by 0.97%. *e results imply that an increase those near the canals. Water shortage is a critical constraint in farmer’s experiences increases the adoption of SRI in the Mwea Irrigation Scheme. Before the inception of SRI, water shortage had forced some farmers to grow rice in technology. As the experience in rice farming increases, smallholders acquire additional skills and knowledge on nonflooded conditions. Similar results were reported by 6 Advances in Agriculture Table 3: Descriptive analysis for the adopters and nonadopters of SRI. Adopters Nonadopters Pooled mean Variable t/chi value n � 259 n � 105 n � 364 ∗ ∗ ∗ Age (mean age) 42 41 41 52.40 Gender Male (%) 22.39 31.43 38.1 3.25 Female (%) 77.61 68.57 73.09 Education level (%) ∗ ∗ ∗ Primary 56.33 43.67 43.41 159.60 Secondary 79.17 20.83 46.15 Postsecondary 97.37 2.63 10.44 ∗ ∗ ∗ Household size 4.12 5.2 5.0 48.08 ∗ ∗ ∗ Farm size (Ha) 1.5 2.1 1.8 15.85 ∗ ∗ ∗ Monthly income (KES) 40374.5200 33761.9000 37,068.21 47.70 ∗ ∗ ∗ Years in paddy farming 6.2 8.1 7.1 27.08 ∗ ∗ ∗ 33.21 Access to extension services 92.66 69.52 81.09 7.34 30.48 18.91 Casual work 88.42 91.43 ∗ ∗ ∗ Livestock keeping 3.86 7.57 89.93 11.36 Others 7.72 1.0 5.72 ∗ ∗ ∗ 33.98 91.43 62.705 98.65 Credit access 66.02 8.57 37.29 ∗ ∗ ∗ Distance to canal 5.4788 4.1714 4.83 3.59 ∗ ∗ Source: authors’ calculations, 2020. Note: denotes significance at 1%. Table 4: Binary logistic regression results. income. *e study recommends to the local government and other stakeholders to focus more on the youthful farmers who Marginal Variables Coefficients Z values are more willing to take up new rice farming technologies such effects as SRI if this practice is to gain prominence in enhancing Gender 1.017 (0.679) 1.5 0.031 production of the crop for better food security. *is should also ∗ ∗ ∗ Age −0.138 (0.043) −3.22 −0.003 apply to smallholder farmers with limited parcels of land who Marital status −3.737 (1.144) −3.27 −0.089 are willing to utilize part of their land for growing rice using Education −0.378 (0.661) −0.57 −0.009 ∗ ∗ ∗ SRI. *ese strategies can best be achieved by the government Household size 1.895 (0.322) 5.88 0.045 ∗ ∗ ∗ Farm size 2.499 (0.498) 5.02 0.059 and other stakeholders in the subsector through enhanced Monthly income 0.000 (0.000) 0.69 0.001 provision of extension services and promoting ease of access to ∗ ∗ ∗ Off-farm work 3.953(0.847) 4.67 0.093 credit facilities to these farmers. Years of rice ∗ ∗ 0.409 (0.121) 5.8 −0.0097 farming ∗ ∗ ∗ Data Availability Extension services 7.809 (1.659) 4.71 0.945 ∗ ∗ ∗ Credit access 8.714 (1.664) 5.24 0.206 ∗ ∗ ∗ *e data supporting the findings of the study are available Distance from canal 1.354 (0.303) 4.47 0.032 upon request from the corresponding author. Cons −8.316 (3.449) −2.41 Number of obs � 364; LR χ2 (11) � 340.60 Prob> χ2 � 0.0000; log like- lihood � -48.378857; pseudo R2 � 0.7788. Source: authors’ calculations, Conflicts of Interest ∗ ∗ ∗ ∗ ∗ 2020. Note: and significance at 1% and 5%. *e authors declare that there are no conflicts of interest. Sinyolo et al. [39], who noted that farmers who were far from the water sources were more efficient in utilizing the re- Acknowledgments sources than farmers who were closer to the water sources. *e authors acknowledge the Higher Education Loans Board 4. 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