DOI: 10.5061/dryad.sbcc2frm1
title: Exploring rates and timing of new agricultural practices with the ADOPT tool

description: This dataset contains data from 22 peer reviewed studies and white papers
 (117 observations), representing 43 countries, that used the Adoption
 Diffusion Outcome Prediction Tool (ADOPT) to predict the adoption and
 diffusion of agricultural innovations between 2014 and 2022. The dataset
 reports predicted peak adoption rate (PPAR (%)) and predicted time to peak
 adoption (PTTP (years)) of various agricultural innovations/practices. The
 agricultural practices from each paper are categorized under 6 broad
 types: technological, new cropping system, new crop/variety, management
 inputs, improved livestock genetics, conservation. Motivation for practice
 adoption is also recorded in the dataset. Motivation for adoption
 was typically explicitly reported in the papers; however, if it was not,
 two members of the research team independently classified the practice
 into one (or more) of the three motivation categories (motivation was
 inferred for four of the 22 studies) and then discussed and reconciled the
 small number of differences. Motivation was categorized as
 "Public" (e.g., environmental motivations referring to
 restoration, climate mitigation goals and/or risk mitigation),
 "Private" (e.g., financial motivations related to yield
 improvements and/or cost savings), or "Public and Private"
 (inclusion of both motivations).  This dataset can be used to
 explore patterns in adoption of various agricultural practices. 
description: # Exploring rates and timing of new agricultural practices with the ADOPT
 tool Dataset DOI:
 [10.5061/dryad.sbcc2frm1](https://doi.org/10.5061/dryad.sbcc2frm1) ##
 Description of the data and file structure ### Files and variables ####
 File: ADOPT_database_FOR_SUBMISSION.xlsx **Description:**  This dataset
 contains data from 22 peer reviewed studies and white papers (117
 observations), representing 43 countries, that used the Adoption Diffusion
 Outcome Prediction Tool (ADOPT) to predict the adoption and diffusion of
 agricultural innovations between 2014 and 2022. ##### Variables Note: Any
 missing values are indicated as "NA" | Column name |
 **Description** | | :----------------------- |
 :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | uniqueID | Unique identifier for each row | | accession | Unique identifier for each study | | citation | Article citation. See paper supplementary file for full citation. | | publication\_date | Year the article was published | | user\_type | The ADOPT tool user (e.g., the person/entity inputting data into the tool) | | population\_type | The population answering the ADOPT tool questions | | tool\_version | Version of the ADOPT tool used in the study | | continent | Continent in which the practice was implemented | | country | Country in which the practice was implemented | | province\_region\_state | Province, region, or state in which the practice was implemented | | agricultural\_innovation | Agricultural practice as described in the article | | innovation\_type | Agricultural innovation categorized into different innovation categories based on best judgement | | goal\_profit |  1 indicates that profit is the motivation for implementing the practice.  | | goal\_environmental |  1 indicates that environmental reasons are the motivation for implementing the practice.  | | goal\_risk\_mitigation |  1 indicates that risk mitigation is the motivation for implementing the practice.  | | goal\_type | Based on the goal categories. If profit was the only motivation, goal type was classified as "Private". If "environmental" or "risk mitigation" was the only motivation,  goal type was classified as "Public". If "profit" and "environmental" and/or  | | goal\_determination | Indicates whether the motivation/goal was explicitly stated in the article. "Indicated" means it was explicitly stated.  "Inferred" meansthe motivation was not explicitly states. Best judgement was used to determine the motivation in this case. See paper for methods. | | PPAR | Predicted peak adoption rate (%) | | PTTP | Predicted time to peak adoption (years) | #### Access information **References included in ADOPT database** * Akroush, S., and B. Dhehibi. 2015. Predicted Willingness of Farmers to Adopt Water Harvesting Technologies: A Case Study from the Jordanian Badia (Jordan). American Eurasian Journal of Agricultural and Environmental Sciences 15: 1502–1513. doi: 10.5829/idosi.aejaes.2015.15.8.12720. - Andrew, R., J. Makindara, S.H. Mbaga, and R. Alphonce. 2019. Ex-Ante Analysis of Adoption of Introduced Chicken Strains Among Smallholder Farmers in Selected Areas of Tanzania. In: Nielsen, P. and Kimaro, H.C., editors, Information and Communication Technologies for Development. Strengthening Southern-Driven Cooperation as a Catalyst for ICT4D. Springer International Publishing, Cham. p. 436–447 * Brown, P.R., U.B. Nidumolu, G. Kuehne, R. Llewellyn, O. Mungai, et al. 2016. Development of the public release version of smallholder ADOPT for developing countries. Australian Centre for International Agricultural Research, Canberra, ACT. - Chesapeake Bay Foundation. 2018. Application of ADOPT (Adoption & Diffusion Outcome Prediction Tool) to Identify Factors Influencing Adoption of Rotational Grazing. Chesapeake Bay Foundation. * Dhehibi, B., A. Nejatian, H. Al-Wahaibi, K. Atroosh, M.S. Al Yafei, et al. 2017. Adoption and Factors Affecting Farmer’s Adoption of Technologies in Farming System: A Case Study of Improved Technologies in ICARDA’s Arabian Peninsula Regional Program. JSD 10(6): 1. doi: 10.5539/jsd.v10n6p1. - Dhehibi, B., M.B. Salah, A. Frija, A. Aw-Hassan, Y.M.A. Raisi, et al., editors. 2018. Predicting Farmers’ Willingness to Adopt Liquid Pollination and Polycarbonate Drying House Technologies: A Case Study from the Date Palm Growers in the Sultanate of Oman. Sustainable Agriculture Research. doi: 10.22004/ag.econ.301837. * Gandorfer, M., S. Schleicher, and K. Erdle. 2018. Barriers to Adoption of Smart Farming Technologies In Germany. Proceedings of the 14th International Conference on Precision Agriculture. Montreal, Quebec, Canada - Greenfeld, A., N. Becker, J.F. Bornman, and D.L. Angel. 2021. Identifying potential adopters of aquaponic farming. Journal of Environmental Planning and Management: 1–19. doi: 10.1080/09640568.2021.1989390. * James, A.R., and M.T. Harrison. 2016. Adoptability and effectiveness of livestock emission reduction techniques in Australia’s temperate high-rainfall zone. Anim. Prod. Sci. 56(3): 393. doi: 10.1071/AN15578. - Kenny, S., and G. Drysdale. 2019. Leucaena-grass pastures and target markets for adoption. Meat and Livestock Australia Limited, North Sydney, New South Wales. * Kotu, B.H., A.R. Nurudeen, F. Muthoni, I. Hoeschle-Zeledon, and F. Kizito. 2022. Potential impact of groundnut production technology on welfare of smallholder farmers in Ghana (G. Kruseman, editor). PLoS ONE 17(1): e0260877. doi: 10.1371/journal.pone.0260877. - Kuehne, G., R. Llewellyn, D.J. Pannell, R. Wilkinson, P. Dolling, et al. 2017. Predicting farmer uptake of new agricultural practices: A tool for research, extension and policy. Agricultural Systems 156: 115–125. doi: 10.1016/j.agsy.2017.06.007. * Ludemann, C. 2022. Estimated annual value of a forage cultivar selection decision tool for New Zealand sheep and beef farmers. Australian Farm Business Management Journal 19. - Marsh, N., R. Eberhard, and B. Gordon. 2021. Incorporating human dimension in water quality modelling: accounting for levels of adoption and co-benefits. Proceedings of the 10th Australian Stream Management Conference * Monjardino, M., J.N.M. Philp, G. Kuehne, V. Phimphachanhvongsod, V. Sihathep, et al. 2020. Quantifying the value of adopting a post-rice legume crop to intensify mixed smallholder farms in Southeast Asia. Agricultural Systems 177: 102690. doi: 10.1016/j.agsy.2019.102690. - Montes de Oca Munguia, O., D.J. Pannell, and R. Llewellyn. 2020. Is it the model or is it the process of using it?: Extension officers evaluate ADOPT as a tool to assist planning in the pastoral sector. Rural Extension and Innovation Systems Journal 16(1): 1–13. doi: 10.3316/informit.365280787172205. * Mwinuka, L., K.D. Mutabazi, F. Graef, S. Sieber, J. Makindara, et al. 2017. Simulated willingness of farmers to adopt fertilizer micro-dosing and rainwater harvesting technologies in semi-arid and sub-humid farming systems in Tanzania. Food Sec. 9(6): 1237–1253. doi: 10.1007/s12571-017-0691-1. - Natcher, D., S. Ingram, R. Solotki, C. Burgess, S. Kulshreshtha, et al. 2021. Assessing the Constraints to the Adoption of Containerized Agriculture in Northern Canada. Front. Sustain. Food Syst. 5: 643366. doi: 10.3389/fsufs.2021.643366. * Pearl, J. 2019. Appendix 14, ADOPT Summary Report. Rural R&D for Profit program Final Report: Improved use of seasonal forecasting to increase farmer profitability. AgriFutures Australia - Powell, J.W., J.M. Welsh, D. Pannell, and R. Kingwell. 2021. Factors influencing Australian sugarcane irrigators’ adoption of solar photovoltaic systems for water pumping. Cleaner Engineering and Technology 4: 100248. doi: 10.1016/j.clet.2021.100248. * Swamila, M., D. Philip, A.M. Akyoo, S. Sieber, M. Bekunda, et al. 2020. Gliricidia Agroforestry Technology Adoption Potential in Selected Dryland Areas of Dodoma Region, Tanzania. Agriculture 10(7): 306. doi: 10.3390/agriculture10070306. - Whiteoak, K., K. Kulkarni, C. Larsen, A.-M. Boland, A. Kelly, et al. 2014. Biogas generation feasibility study. Horticulture Australia Ltd.