Adoption Determinants of Agricultural Extension Communication Channels in Emergency and Non-emergency Situations in Ghana

Abstract

The determinants of agricultural extension communication channel usage depend on several factors but have not been explored in the context of emergency (Fall Armyworm outbreak) and non-emergency situations. A multistage purposive sampling technique was used to select 318 farmers, focusing on the districts and the communities with the highest reported cases of fall armyworm infestation. Descriptive statistics and a multivariate probit analysis were used in the analysis of the data. The results showed an increase in the intensity of channels' usage in the emergency situation. It was also found that fertilizer and improved seeds were the most common extension need. Channels are found to be more complementary in the emergency case. In both situations, some factors explaining the choices of these channels varied, while others agreed. The study recommends multiple channels are made available and that time-consuming channels irrespective of their effectiveness should be avoided by extensionists in emergency situations.

Optimalisasi Komunikasi Penyuluh Pertanian dalam Aktivitas Penyuluhan

“…Selain itu dalam menjalankan tugas, peran, dan tanggung jawab penyuluh dalam melaksanakan aktivitas penyuluhan berpedoman pada Undang-Undang Nomor 16 Tahun 2006 tentang Sistem Penyuluhan yang menyangkut penyuluh itu sendiri, materi penyuluhan, media penyuluhan, sasaran penyuluhan ke petani serta dampak positifnya terhadap petani (Undang-Undang Republik Indonesia Nomor 16 Tentang Sistem Penyuluhan Pertanian, Perikanan, 2006), sehingga penelitian ini jauh lebih komplek dibandingkan penelitian sebelumnya. Penelitian empiris sebelumnya menerapkan pendekatan teori model komunikasi interpersonal SMCR (Source, Message, Channel, Receiver) (Ambar, 2022) yang dikemukakan Kenneth Berlo (Abukari et al, 2021); (Al-Zahrani et al, 2017); Bidireac et al, 2015;Cook et al, 2021;Khusna et al, 2018;Permana et al, 2011;Ruyadi, 2017;Wulandari, 2015) dan model Kohlberg (Fathurohman & Romalasari, 2017). Penelitian ini juga menggunakan analisis MDS-Rafish yang berbeda dengan penelitian terdahulu yang telah dilakukan banyak menggunakan analisis deskriptif, rank spearman, MSI, regresi biasa, atau pun regresi linier berganda.…”

The complementarity and substitutability of sustainable agricultural practices among maize farm households under AFRINT regions in Ghana: Do the socioeconomic determinants confirm these?

“…In MVP, we recognize the correlations in the error terms among the adoption equations (in our case 10 equations) and jointly estimate the set of the ten probit models. In this case, the use of the univariate probit or logit model becomes inappropriate due to the inter-relationship among SAPs' technologies since probit or logit ignores such correlation in the error terms (Abukari et al, 2021;Ehiakpor et al, 2021). This correlation may emanate from the fact that the same latent variables could influence the adoption of different SAPs.…”

“…We follow studies such as Ehiakpor et al (2021) and Abukari et al (2021) to formulate the multivariate model which has 10 dependent variables,…”