Extension Contact and Rice Productivity of Non-Swamp Farmer Groups in Mamasa Regency, West Sulawesi

https://doi.org/10.52045/jca.v7i1.1505

Authors

  • Muhammad Ridwan Faculty of Vocational Studies, Hasanuddin University, Makassar, 90245, South Sulawesi, Indonesia and Applied Agriculture Research Group, Makassar, Indonesia
  • Andi Syastiawan Faculty of Vocational Studies, Hasanuddin University, Makassar, 90245, South Sulawesi, Indonesia
  • Andi Emelda Faculty of Vocational Studies, Hasanuddin University, Makassar, 90245, South Sulawesi, Indonesia

Keywords:

Agricultural extension , extension contact, farmer groups, lowland rice, Mamasa Regency

Abstract

Rice production in West Sulawesi fell by 16.83% in 2023, driven mainly by a 14.71% contraction in harvested area, while implied provincial productivity declined only slightly (from about 5.10 to 4.97 t ha−1). Extension services are expected to raise productivity, yet their association with yields in heterogeneous highland rice systems is poorly documented. This study examined the cross-sectional association between extension contact and rice productivity among non-swamp farmer groups in Mamasa Regency, defined as lowland rice (sawah) groups outside tidal and inland swamps, cultivating rainfed, semi-technically irrigated, or technically irrigated fields. Field-survey data from 179 farmer groups in all 17 sub-districts were analyzed. Extension exposure was measured by three categorical items (meeting regularity, frequency of contact with extension workers, and mentoring activeness) and productivity as the chairperson-reported mean yield across cultivated seasons. Because a reflective latent specification showed inadequate reliability (ρc=0.629; AVE=0.411) and a redundant outcome indicator, the primary analysis used regression on the observed indicators with robust and bootstrap confidence intervals, with PLS-SEM as a secondary check. Routine contact with extension workers (88.3% of groups) was associated with 1.59 t ha−1 lower productivity than rare or occasional contact (95% CI −2.65 to −0.54; p=0.003), whereas meeting regularity and mentoring activeness showed no association. The association was stable across alternative outcome definitions, influential-case exclusions, sub-district fixed effects, and PLS-SEM specifications but attenuated and became non-significant after exploratory adjustment for agroecological, technology-access, and group covariates (−0.88; 95% CI −2.05 to 0.29). Out-of-sample predictive accuracy was low (Q2predict=0.070 against a naive mean benchmark). The negative association should not be read as a harmful effect of extension; need-based targeting, poorly matched extension content, missing complementary inputs, and unmeasured agroecological differences remain competing hypotheses. Documenting targeting criteria, collecting baseline productivity and agroecological data, and evaluating context-specific extension packages over time are recommended.

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References

Anderson JR & Feder G. 2004. Agricultural extension: Good intentions and hard realities. The World Bank Research Observer. 19(1):41-60. https://doi.org/10.1093/wbro/lkh013

Andrias. 2018. Pengaruh luas lahan terhadap produksi dan pendapatan usahatani padi sawah (Suatu kasus di Desa Jelat Kecamatan Baregbeg Kabupaten Ciamis). Jurnal Ilmiah Mahasiswa AGROINFO GALUH. 5(1):785-793.

Austin PC. 2009. Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Statistics in Medicine. 28(25):3083-3107. https://doi.org/10.1002/sim.3697

[BBPP Ketindan] Balai Besar Pelatihan Pertanian Ketindan. 2025. Melalui SID, optimasi lahan non rawa siap diwujudkan untuk pertanian berkelanjutan. Available at: https://bbppketindan.bppsdmp.pertanian.go.id/blog/post/melalui-sid-optimasi-lahan-non-rawa-siap-diwujudkan-untuk-pertanian-berkelanjutan [accessed 11 September 2026].

Birkhaeuser D, Evenson RE & Feder G. 1991. The economic impact of agricultural extension: A review. Economic Development and Cultural Change. 39(3):607-650. https://doi.org/10.1086/451893

[BPS] Badan Pusat Statistik Kabupaten Mamasa. 2023. Pada 2023, luas panen padi di Sulawesi Barat diperkirakan sebesar 59,12 ribu hektare dengan produksi padi sekitar 294,03 ribu ton gabah kering giling (GKG). Available at: https://mamasakab.bps.go.id/id/pressrelease/2023/11/02/684/ [accessed 16 July 2026].

Cameron AC, Gelbach JB & Miller DL. 2008. Bootstrap-based improvements for inference with clustered errors. The Review of Economics and Statistics. 90(3):414-427. https://doi.org/10.1162/rest.90.3.414

Diamantopoulos A & Winklhofer HM. 2001. Index construction with formative indicators: An alternative to scale development. Journal of Marketing Research. 38(2):269-277. https://doi.org/10.1509/jmkr.38.2.269.18845

Efron B & Tibshirani RJ. 1993. An Introduction to the Bootstrap. New York (US): Chapman & Hall.

Feder G, Murgai R & Quizon JB. 2004. Sending farmers back to school: The impact of farmer field schools in Indonesia. Review of Agricultural Economics. 26(1):45-62. https://doi.org/10.1111/j.1467-9353.2003.00161.x

Fornell C & Larcker DF. 1981. Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research. 18(1):39-50. https://doi.org/10.2307/3151312

Gourlay S, Kilic T & Lobell DB. 2019. A new spin on an old debate: Errors in farmer-reported production and their implications for inverse scale-productivity relationship in Uganda. Journal of Development Economics. 141:102376. https://doi.org/10.1016/j.jdeveco.2019.102376

Haefele SM, Nelson A & Hijmans RJ. 2014. Soil quality and constraints in global rice production. Geoderma. 235-236:250-259. https://doi.org/10.1016/j.geoderma.2014.07.019

Hair JF, Risher JJ, Sarstedt M & Ringle CM. 2019. When to use and how to report the results of PLS-SEM. European Business Review. 31(1):2-24. https://doi.org/10.1108/EBR-11-2018-0203

Henseler J, Ringle CM & Sarstedt M. 2015. A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science. 43(1):115-135. https://doi.org/10.1007/s11747-014-0403-8

Hernalius LA, Sumardjo S & Hamzah H. 2018. Pengaruh penyuluhan pertanian terhadap tingkat produktivitas padi sawah. Jurnal Sains Komunikasi dan Pengembangan Masyarakat (JSKPM). 2(3):279-288. https://doi.org/10.29244/jskpm.2.3.279-288

Hidayati F, Syahni R, Suliansyah I & Tanjung HB. 2025. Model teoritis adopsi inovasi pertanian: Integrasi pendekatan perilaku petani. Rawa Sains: Jurnal Sains STIPER Amuntai. 15(1):70-79.

Jamil MH, Tika N, Fudjaja L, Tenriawaru AN, Salam M, Ridwan M, Muslim AI & Chand NV. 2023. Effectiveness of agricultural extension on paddy rice farmer's Baubau City, Southeast Sulawesi, Indonesia. Sustainability. 15(4):3773. https://doi.org/10.3390/su15043773

Jarvis CB, MacKenzie SB & Podsakoff PM. 2003. A critical review of construct indicators and measurement model misspecification in marketing and consumer research. Journal of Consumer Research. 30(2):199-218. https://doi.org/10.1086/376806

Khandker SR, Koolwal GB & Samad HA. 2010. Handbook on Impact Evaluation: Quantitative Methods and Practices. Washington DC (US): The World Bank. https://doi.org/10.1596/978-0-8213-8028-4

Kock N & Hadaya P. 2018. Minimum sample size estimation in PLS-SEM: The inverse square root and gamma-exponential methods. Information Systems Journal. 28(1):227-261. https://doi.org/10.1111/isj.12131

Koenker R & Bassett G. 1978. Regression quantiles. Econometrica. 46(1):33-50. https://doi.org/10.2307/1913643

Kumar N, Stern LW & Anderson JC. 1993. Conducting interorganizational research using key informants. Academy of Management Journal. 36(6):1633-1651. https://doi.org/10.5465/256824

Latif A, Ilsan M & Rosada I. 2022. Hubungan peran penyuluh pertanian terhadap produktivitas petani padi. Wiratani: Jurnal Ilmiah Agribisnis. 5(1):1-9.

Long JS & Ervin LH. 2000. Using heteroscedasticity consistent standard errors in the linear regression model. The American Statistician. 54(3):217-224. https://doi.org/10.1080/00031305.2000.10474549

Podsakoff PM, MacKenzie SB, Lee JY & Podsakoff NP. 2003. Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology. 88(5):879-903. https://doi.org/10.1037/0021-9010.88.5.879

Rogers EM. 2003. Diffusion of Innovations. 5th ed. New York (US): Free Press.

Salam M, Jamil MH, Tenriawaru AN, Kamarulzaman NH, Syam SH, Rahmadanih, Ramadhani A, et al. 2024. The effectiveness of agricultural extension in rice farming in Bantaeng Regency, Indonesia: Employing structural equation modeling in search for the effective ways in educating farmers. Journal of Agriculture and Food Research. 18:101487. https://doi.org/10.1016/j.jafr.2024.101487

Shmueli G, Sarstedt M, Hair JF, Cheah JH, Ting H, Vaithilingam S & Ringle CM. 2019. Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict. European Journal of Marketing. 53(11):2322-2347. https://doi.org/10.1108/EJM-02-2019-0189

Takahashi K, Muraoka R & Otsuka K. 2020. Technology adoption, impact, and extension in developing countries' agriculture: A review of the recent literature. Agricultural Economics. 51(1):31-45. https://doi.org/10.1111/agec.12539

Webb MD. 2023. Reworking wild bootstrap-based inference for clustered errors. Canadian Journal of Economics. 56(3):839-858. https://doi.org/10.1111/caje.12661

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Published

2026-10-29

How to Cite

Ridwan, M., Syastiawan, A., & Emelda, A. (2026). Extension Contact and Rice Productivity of Non-Swamp Farmer Groups in Mamasa Regency, West Sulawesi. CELEBES Agricultural, 7(1), 13–37. https://doi.org/10.52045/jca.v7i1.1505

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