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The Journal of ASFMRA has a long-standing tradition of sharing farm management ideas and publishing the results of academic studies. A keyword analysis of titles from 330 published articles was conducted, along with an evaluation of the respective authors and their institutions. Comparisons between articles and the Gold Quill winners are discussed. Results of this analysis are of interest to authors considering submitting manuscripts to the Journal of ASFMRA and anyone interested in farm management and rural appraisal

Griffin, T.W. and Gammon, S. 2020, Journal of the American Society of Farm Managers and Rural Appraisers 

Dog in Farm
Combine Harvester on Field

Precision technologies have been available at the farm level for decades. Some technologies have been readily adopted, while the adoption of other technologies has been slower. The purpose of this study is to examine the factors influencing farmers' time-to-adoption decisions as duration between year of commercialization of precision agriculture (PA) technologies and year of adoption, at the farm level. Time-to-adoption, which is the difference in years between technologies becoming commercially available and the year of adoption was determined using non-parametric duration analysis, and the impact of specific farm/farmer characteristics on time-to-adoption were estimated using a semi-parametric Cox proportional-hazard (CPH) model, based on a panel dataset of 316 Kansas farms from 2002 to 2018.

Ofori, E., Griffin, T., Yeager, E. 2020, Agricultural Finance Review

Precision agriculture has renewed the interest of farmers and researchers to conduct on‐farm planned comparisons and researchers with respect to field‐scale research. Cotton (Gossypium hirsutum L.) yield monitor data collected on‐the‐go from planned field‐scale on‐farm experiments can be used to make improved decisions if analyzed appropriately. When farmers and researchers compare treatments implemented at larger block designs, treatment edge effects and spatial externalities need to be considered so that results are not biased. Spatial analysis methods are compared for field‐scale research using site‐specific data, paying due attention to local and global patterns of spatial correlation. Local spatial spillovers are explicitly modeled by spatial statistical techniques that led to improved farm management decisions in combination with the limited replication strip trial data farmers currently collect.

Griffin, T.W., Fitzgerald, G.J., Lowenberg‐DeBoer, J. ,Barnes, E.M. 2020, Agronomy Journal 

Worlds End South Africa
Hand Feeding Elephant

Data visualization has become important to farm management and commodity marketing during recent price and weather phenomena. Accessing and evaluating United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) data via software tools empowers rural property professionals to mitigate risk. Data acquisition and visualization examples include days suitable for fieldwork (DSFW) during peanut planting and harvest in 11 peanut producing states. The overall objective was to share techniques to access and analyze publicly available data. Specific objectives were to demonstrate software tools to define most active dates for field activities, estimate DSFW during most active dates for each state, and assess DSFW time trends. Analytic results are important for machinery selection and acreage allocation. Software tools have been made available for readers to use in their own applied research.

Griffin, T.W. and Gammon, S. 2020, Journal of the American Society of Farm Managers and Rural Appraisers 

Precision technologies such as variable rate fertilizer applications have been touted as solving production agricultural issues, increasing yields, and improving environmental stewardship. Variable rate technologies have been widely available on farms especially via custom service providers; however, utilization rates of these technologies remain relatively low. Low adoption rates indicate barriers to adoption and an opportunity for market expansion. The degree to which such technologies may reduce the demand for fertilizers will depend on VRT adoption rates.


Griffin, T.W. and Traywick, L.S. 2020, Journal of Applied Farm Economics, 3(2):59-67 DOI: 10.7771/2331-9151.1049

Hands in the Soil
Surveyor Engineer

Digital agriculture and the utilization of technology on the farm has garnered increased attention in recent years. Farmers, lenders, advisors, and researchers frequently ask whether additional technology can increase productivity and the resulting profitability of the farm operation, and lenders and marketers ask whether they should focus on the demographics of their customers differently—considering, for example, how different generations respond to or adopt new technology. This paper looks at the adoption of various precision agriculture technologies by Kansas farms and breaks the adoption down by sole proprietor and multiple-operator farms. We find that adoption indeed varies across generations as well as by generation mix for multiple-generation farms. We also predict that the current younger generation will control the majority of farm operations at an older age than previous generations.


Griffin, T.W., Traywick, L.S., Yeager, E.A. 2020, Federal Reserve Bank of Kansas City Ag Symposium Research Article 29:50

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