TECHNOLOGY ADOPTION BY SMALLHOLDER FISH FARMERS

Introduction

Aquaculture technologies, in terms of equipment and machinery, have supported the increasing productivity of aquaculture. However, the benefits of aquaculture technologies depend on the adoption by fish farmers. Previous studies revealed that the adoption level of management practices by smallholder fish farmers is low to moderate. Previous studies also found that farm and farmer characteristics were associated with the adoption of management practices among smallholder fish farmers (Islam et al., 2024; Muddassir et al., 2019; Susilo et al., 2019). Despite this, most studies have analyzed the adoption of management practices in aquaculture, but rather limited studies have studied the adoption of aquaculture technologies. Furthermore, smallholder fish farmers dominate global aquaculture and play an important role in generating household income and rural socioeconomic development (Aung et al., 2023; Mantey et al., 2020). Despite their importance, technology adoption among smallholder fish farmers remains under-researched. This study aimed to understand the adoption level and determine the variables associated with technology adoption among inland smallholder fish farmers.

Material and Method

Purposive sampling was used to select Tilapia fish farmers in Ciamis, Indonesia. Tilapia was the most produced inland aquaculture product in Indonesia. The survey was conducted by face-to-face interviews. Descriptive analysis was conducted on fish farmers' characteristics and aquaculture technology adoption. To identify the variables associated with the adoption of aquaculture technology, multinomial logistic regression analysis was employed based on (Agresti, 2013). Moreover, backward selection was conducted to select the independent variables (Mendenhall & Sincich, 2012).

Result and Discussion

Overall, the adoption of aquaculture technology levels was low. Aerators (blowers), biofloc, and paddle wheels were adopted by about 20%, 14%, and 10% of farmers, respectively. Smart technologies such as water quality sensors, smart feeders, and aerators with oxygen sensors had lower adoption levels than previously mentioned technologies (1.5%, 0%, and 3.1%, respectively). This study revealed that fish farming experience, education, and subdistrict were significantly associated with the adoption of aquaculture technology. The findings of this study suggest that policies aimed at increasing technology adoption should account both farmers' capacities and local environmental conditions. Accordingly, the study recommends: (1) prioritizing government technology programs in the region with moderate to poor water quality, (2) improving access to financial resources, (2) strengthening knowledge dissemination, and (3) providing intensive training to support proper application of aquaculture technologies.

Table 5. Result of multivariable multinomial logistic regression model: associated variables to aquaculture technology adoption (no adoption vs. dis-adoption vs. current adoption, n=196)

Variable and category

Dis-adoption vs No adoption

Current adoption vs No adoption

ME

Coef. (SE)

95% conf. interval

ME

Coef. (SE)

95% conf. interval

Experience

0.01

0.05 (0.02)**

0.01-0.09

0.001

0.02 (0.02)

-0.018 - 0.06

Education

Elementary school

Ref.^a

Ref.^a

High school

0.03^a

0.43 (0.59)

-0.74 - 1.60

0.04^a

0.45 (0.55)

-0.62-1.53

University

0.20^b

1.79 (0.68)**

0.46 - 3.11

0.08^a

1.16 (0.67)

-0.15 - 2.47

Subdistrict

Kawali

Ref.^a

Ref.

Ref.^a

Baregbeg

0.12^a

0.90 (0.82)

-0.70 - 2.51

-0.04^a

0.02 (0.68)

-1.30 - 1.35

Cijeungjing

0.09^a

0.95 (1.09)

-1.18 - 3.09

0.07^a

0.57 (0.84)

-1.08 - 2.22

Cikoneng

0.21^a

1.24 (0.97)

-0.66 - 3.14

-0.11^a,c

-0.29 (0.98)

-2.20 - 1.63

Cipaku

0.03^a

-0.06 (0.80)

-1.63 - 1.52

-0.19^a,d

-1.32 (0.72)

-2.73 - 0.09

Lumbung

0.03^a

0.14 (1.06)

-1.94 - 2.21

-0.06^a,e

-0.31 (0.85)

-1.99 - 1.36

Sadananya

-0.05^a

-0.97 (0.90)

-2.73 - 0.80

-0.20^a,f

-1.56 (0.71)*

-2.96 - -0.16

Sindangkasih

-0.004^a

-0.48 (0.91)

-2.26 - 1.29

-0.25^b,c,d,e,f

-2.41 (1.15)*

-4.67 - -0.16

Note: independent variable selection method: backward selection; marginal effect displayed was the average marginal effect (AME) from all observations; n = 196; all VIF values < 5; prob > Chi2 =0.0039; McFadden's R2: 0.122; a, b, c, d, e, f: differ superscripts indicates differ value within column, **: significant at P-value < 0.01, *: significant at P-value < 0.05

Acknowledgement

The authors would like to express our gratitude LPDP as a research funding, the smallholder fish farmers participated in the survey, Smart Indonesian Agriculture (Smart-In-Ag) project WUR, the fisheries and aquaculture extension workers and Dinas Peternakan dan Perikanan, Kabupaten Ciamis for their support in the data collection process.

References

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