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Agricultural Technology Adoption Models
Research Guide

What is Agricultural Technology Adoption Models?

Agricultural Technology Adoption Models analyze factors influencing farmers' uptake of innovations like precision agriculture tools, climate-smart practices, and digital apps using frameworks such as diffusion theory, TOE, and UTAUT.

These models employ econometric methods to identify barriers including credit access, extension services, and social networks. Studies focus on regions like Indonesia and West Africa, with over 20 papers since 2013 examining CSA and ICM-FFS adoption (Kariyasa et al., 2013; Ouédraogo et al., 2019). Key metrics include adoption rates derived from Average Treatment Effect frameworks.

Curated Papers

Key Challenges

Why It Matters

Models guide scaling of yield-boosting technologies in developing regions, reducing productivity gaps; for instance, Kariyasa et al. (2013) identified factors boosting rice self-sufficiency in Indonesia via ICM-FFS (85 citations). Ouédraogo et al. (2019) quantified CSA adoption drivers in Mali, informing policy for 84% potential uptake (84 citations). Zhao et al. (2023) reviewed CSA advancements, linking adoption to GHG reduction and food security (103 citations).

Key Research Challenges

Heterogeneous Farmer Barriers

Farmers face diverse constraints like credit and extension access, varying by region and crop. Kariyasa et al. (2013) used logistic regression to show education and farm size effects on ICM-FFS adoption in swampy areas. Addressing this requires context-specific models beyond universal frameworks.

Measuring Actual vs Potential Adoption

Distinguishing realized from latent adoption rates challenges policy design. Ouédraogo et al. (2019) applied Average Treatment Effect to estimate 84% potential CSA uptake in Mali. Data gaps on latent demand hinder scaling interventions.

Extension Service Ineffectiveness

Top-down extension fails against complex agroclimatic risks. Osumba et al. (2021) advocated bottom-up Farmer Field Schools for resilience (78 citations). Integrating digital tools like mobile apps remains underexplored (Khan et al., 2020).

Essential Papers

A Review of Climate-Smart Agriculture: Recent Advancements, Challenges, and Future Directions

Junfang Zhao, Dongsheng Liu, Ruixi Huang · 2023 · Sustainability · 103 citations

Global climate change has posed serious threats to agricultural production. Reducing greenhouse gas (GHG) emissions and ensuring food security are considered the greatest challenges in this century...

ANALYSIS OF FACTORS AFFECTING ADOPTION OF INTEGRATED CROP MANAGEMENT FARMER FIELD SCHOOL (ICM-FFS) IN SWAMPY AREAS

Ketut Kariyasa, Yovita Anggita Dewi, Kariyasa, Ketut et al. · 2013 · AgEcon Search (University of Minnesota, USA) · 85 citations

The main target of Integrated Crop Management Farmer Field School (ICM-FFS) development is to boost rice production in order to accelerate the achievement of sustainable rice self-sufficient in Ind...

Uptake of Climate-Smart Agricultural Technologies and Practices: Actual and Potential Adoption Rates in the Climate-Smart Village Site of Mali

Mathieu Ouédraogo, Prosper Houessionon, Robert B. Zougmoré et al. · 2019 · Sustainability · 84 citations

Understanding the level of adoption of Climate-Smart Agriculture (CSA) technologies and practices and its drivers is needed to spur large-scale uptake of CSA in West Africa. This paper used the Ave...

Transforming Agricultural Extension Service Delivery through Innovative Bottom–Up Climate-Resilient Agribusiness Farmer Field Schools

Joab Osumba, John Recha, George Oroma · 2021 · Sustainability · 78 citations

Conventional approaches to agricultural extension based on top–down technology transfer and information dissemination models are inadequate to help smallholder farmers tackle increasingly complex a...

Faktor-faktor yang Mempengaruhi Petani dalam Menerapkan Standar Operasional Prosedur (SOP) Sistem Pertanian Organik di Kabupaten Bandung Barat

Anne Charina, Rani Andriani Budi Kusumo, Agriani Hermita Sadeli et al. · 2018 · Jurnal Penyuluhan · 53 citations

<p><em>Di Kabupaten Bandung Barat banyak petani sayuran yang mulai menjalankan pertanian organic, namun masih terdapat petani yang belum sepenuhnya menjalankan sistem budidaya sayuran o...

Stepping up from subsistence to commercial intensive farming to enhance welfare of farmer households in <scp>Indonesia</scp>

Joko Mariyono · 2019 · Asia & the Pacific Policy Studies · 48 citations

Abstract This article assesses the welfare impact of intensive chilli farming and determines the factors motivating farmers to engage in commercial farming. This study uses a structural equation mo...

Analyzing mobile phone usage in agricultural modernization and rural development

Nawab Khan, Badar Naseem Siddiqui, Nanak Khan et al. · 2020 · International Journal of Agricultural Extension · 42 citations

The agricultural sector worldwide is facing many issues relating to crop productivity due to the lack of communication between extension workers and farmers. To reduce this gap, information technol...

Reading Guide

Foundational Papers

Start with Kariyasa et al. (2013, 85 citations) for ICM-FFS logistic models in Indonesia; Effendy et al. (2013) analyzes farmer traits on cocoa tech adoption.

Recent Advances

Zhao et al. (2023, 103 citations) reviews CSA challenges; Ouédraogo et al. (2019, 84 citations) quantifies Mali potentials; Osumba et al. (2021, 78 citations) on resilient field schools.

Core Methods

Logistic/multinomial regression for binary adoption (Kariyasa 2013); Average Treatment Effect for causal rates (Ouédraogo 2019); structural equation modeling for welfare paths (Mariyono 2019).

How PapersFlow Helps You Research Agricultural Technology Adoption Models

Discover & Search

Research Agent uses searchPapers and exaSearch to query 'climate-smart agriculture adoption models Indonesia' yielding Kariyasa et al. (2013, 85 citations); citationGraph maps connections to Ouédraogo et al. (2019) and findSimilarPapers uncovers regional analogs like Mariyono (2019).

Analyze & Verify

Analysis Agent applies readPaperContent on Kariyasa et al. (2013) to extract logistic regression coefficients; verifyResponse with CoVe checks econometric claims against raw data, runPythonAnalysis replays adoption models via pandas for GRADE A statistical verification.

Synthesize & Write

Synthesis Agent detects gaps in CSA extension models from Zhao et al. (2023); Writing Agent uses latexEditText for model equations, latexSyncCitations integrates 10+ papers, latexCompile generates polished reports with exportMermaid for adoption pathway diagrams.

Use Cases

"Replicate logistic regression from Kariyasa 2013 ICM-FFS adoption study"

Research Agent → searchPapers → Analysis Agent → readPaperContent + runPythonAnalysis (pandas logistic model) → matplotlib adoption rate plot output.

"Write LaTeX review of CSA adoption barriers in Africa and Indonesia"

Research Agent → citationGraph (Ouédraogo 2019 hub) → Synthesis → gap detection → Writing Agent → latexEditText + latexSyncCitations + latexCompile → PDF with diagrams.

"Find GitHub repos analyzing mobile app adoption in agriculture like Khan 2020"

Research Agent → paperExtractUrls (Khan 2020) → Code Discovery → paperFindGithubRepo → githubRepoInspect → verified code for econometric replication.

Automated Workflows

Deep Research workflow scans 50+ adoption papers via searchPapers → citationGraph clustering → structured report on TOE/UTAUT applications. DeepScan's 7-step chain verifies Kariyasa et al. (2013) factors with CoVe checkpoints and runPythonAnalysis. Theorizer generates hypotheses linking extension participation (Anwarudin 2019) to welfare impacts.

Try Doxa for Agricultural Technology Adoption Models Research

Frequently Asked Questions

What defines Agricultural Technology Adoption Models?

Models apply diffusion theory, TOE framework, and UTAUT to quantify factors like education and credit driving uptake of tools such as ICM-FFS and CSA practices (Kariyasa et al., 2013).

What are common methods in these models?

Logistic regression and Average Treatment Effect estimate adoption probabilities; Kariyasa et al. (2013) used multinomial logit for ICM-FFS, Ouédraogo et al. (2019) applied ATE for CSA rates.

What are key papers on this topic?

Foundational: Kariyasa et al. (2013, 85 citations) on ICM-FFS; recent: Zhao et al. (2023, 103 citations) CSA review, Ouédraogo et al. (2019, 84 citations) Mali uptake.

What open problems exist?

Gaps include digital service integration (Simelton 2021) and gender-disaggregated models (Mulyaningsih 2018); measuring latent adoption beyond surveys remains unresolved.