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Organizational Knowledge Transfer between Brazilian and U.S. Agriculture: A Comparative Review with Applications for Texas Agribusiness Consulting
Subject area: Science,Engineering and Technology · Area of research: Sciences
Abstract
Purpose. To compare the organizational capabilities of Brazilian and U.S. commercial agriculture, identify management practices that can be transferred from Brazil to mid-size farms in Texas (and vice versa), and describe how specialized consulting can carry out that transfer. Design / methodology / approach. A comparative review combining the knowledge-transfer literature (tacit and explicit knowledge, "stickiness", absorptive capacity) with secondary sources on the two agricultural systems and with the author's professional experience in commercial farm management and agricultural inspection in Brazil and in agribusiness consulting in South-Central Texas. Practices are evaluated with a transferability matrix and a five-stage transfer pipeline. Findings. The two systems have complementary strengths. Brazilian commercial agriculture, shaped by rapid expansion in the Cerrado under lean staffing, developed strong disciplines in written field monitoring, seasonal planning, multi-party coordination and record retrievability. Texas agriculture benefits from dense input and service networks, widespread precision technology, mature commercial documentation and robust risk-management instruments. Three Brazilian practices transfer readily — structured field-monitoring protocols, explicit role definition in multi-party arrangements, and a single maintained record repository — because they depend on organizational principles rather than on Brazilian technology or regulation. Practical implications. Consulting that systematizes resources Texas farms already have can improve management consistency, reduce administrative friction and clarify export or partnership pathways without large capital outlays. Originality / value. The review shifts the Brazil–U.S. comparison from commodities and competition to organizational knowledge, and it offers a practical method for cross-border transfer at the farm level.
Keywords
absorptive capacity, agribusiness consulting, Brazil, Cerrado, cross-border agriculture, farm management, knowledge transfer, organizational practices, Texas, United States
References
[1] Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128–152.
[2] Embrapa. (2022). Agricultural research and technology transfer in Brazil. Brazilian Agricultural Research Corporation.
[3] FAO. (2020). The state of food and agriculture 2020: Overcoming water challenges in agriculture. Food and Agriculture Organization of the United Nations.
[4] Klerkx, L., & Leeuwis, C. (2009). Establishment and embedding of innovation brokers at different innovation system levels. Technological Forecasting and Social Change, 76(6), 849–860.
[5] Mongabay. (2020). Soy and the Cerrado. https://news.mongabay.com
[6] Nonaka, I., & Takeuchi, H. (1995). The knowledge-creating company. Oxford University Press.
[7] OECD. (2021). Agricultural policy monitoring and evaluation 2021. OECD Publishing.
[8] Reuters. (n.d.). Brazil's soy boom. https://www.reuters.com
[9] Szulanski, G. (1996). Exploring internal stickiness: Impediments to the transfer of best practice within the firm. Strategic Management Journal, 17(S2), 27–43.
[10] USDA Foreign Agricultural Service. (2023). Brazil: Grain and feed annual. U.S. Department of Agriculture.
How to cite this paper
@article{1724181,
author = {Walter Luiz Ribeiro Adriano},
title = {Organizational Knowledge Transfer between Brazilian and U.S. Agriculture: A Comparative Review with Applications for Texas Agribusiness Consulting},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {9},
number = {6},
pages = {2697-2703},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1724181.pdf},
abstract = {Purpose. To compare the organizational capabilities of Brazilian and U.S. commercial agriculture, identify management practices that can be transferred from Brazil to mid-size farms in Texas (and vice versa), and describe how specialized consulting can carry out that transfer.
Design / methodology / approach. A comparative review combining the knowledge-transfer literature (tacit and explicit knowledge, "stickiness", absorptive capacity) with secondary sources on the two agricultural systems and with the author's professional experience in commercial farm management and agricultural inspection in Brazil and in agribusiness consulting in South-Central Texas. Practices are evaluated with a transferability matrix and a five-stage transfer pipeline.
Findings. The two systems have complementary strengths. Brazilian commercial agriculture, shaped by rapid expansion in the Cerrado under lean staffing, developed strong disciplines in written field monitoring, seasonal planning, multi-party coordination and record retrievability. Texas agriculture benefits from dense input and service networks, widespread precision technology, mature commercial documentation and robust risk-management instruments. Three Brazilian practices transfer readily — structured field-monitoring protocols, explicit role definition in multi-party arrangements, and a single maintained record repository — because they depend on organizational principles rather than on Brazilian technology or regulation.
Practical implications. Consulting that systematizes resources Texas farms already have can improve management consistency, reduce administrative friction and clarify export or partnership pathways without large capital outlays.
Originality / value. The review shifts the Brazil–U.S. comparison from commodities and competition to organizational knowledge, and it offers a practical method for cross-border transfer at the farm level.},
keywords = {absorptive capacity, agribusiness consulting, Brazil, Cerrado, cross-border agriculture, farm management, knowledge transfer, organizational practices, Texas, United States},
month = {December},
}