Darrell A. Dromgoole, Associate Professor and Extension Specialist, Texas A&M AgriLife Extension Service.
Brent Batchelor, Regional Program Leader for Agriculture and Natural Resources/4-H and Youth Development, Central Region, Texas A&M AgriLife Extension Service.
Scott Cummings, Associate Department Head and Program Leader; Professor and Extension Specialist, Texas A&M AgriLife Extension Service.
Stacey Dewald, Graduate Assistant, Texas A&M AgriLife Extension Service
Michelle Payne, Extension Program Specialist I, Texas A&M AgriLife Extension Service
Throughout the course of Extension history educators have attempted to determine factors that lead to clientele adopting various agricultural practices. Various theories have been advantageous when attempting to determine adoption of agricultural practices. Rogers’ (2003) Diffusion of Innovation focuses on how personal characteristics, the characteristics of the innovation, and time predict the behavior of individuals with regards to adopting new technologies. Other theories focus more explicitly on trying to understand the relationship between producers’ attitudes and their behaviors. The Theory of Planned Behavior/Reasoned Action Approach (Fishbein and Ajzen 2011) examines how attitudes, subjective norms, and perceived behavioral control all influence behavioral intentions and then behavior.
Researchers from Purdue University conducted a statewide survey of 1,320 agricultural landowners and producers in Indiana in early 2014 to collect information about awareness and usage of nutrient management practices (Ulrich-Schad,Jalon, Babin & Prokopy, 2017). The researchers in this study utilized this data to explore the determinants of farmers’ usage of four nutrient best management practices and determine whether a more precise measurement of practice adoption could be determined (Ulrich-Schad, et al., 2017).
An extensive body of research has inquired about the motivations and barriers to producers’ adoption of a broader swath of conservation practices (Knowler & Bradshaw 2007; Baumgart-Getz et al. 2012). However, despite growing concerns in the mid-west and other parts of the country about the impact of agricultural nonpoint sources pollution on water resources, research specifically focused on nutrient management decision-making among producers remains relatively limited (Ulrich-Schad, et al., 2017). Furthermore, factors that predict the adoption of nutrient best management practices have varied from study to study and by the practice under examination (Ulrich-Schad,et al., 2017). Studies examining the adoption of nutrient best management practices also tend to examine simply whether a farmer has adopted a practice or not, rather than the extent to which they have implemented it (Reimer et al. 2014).
Prokopy , Floress,, Kllatthar-Weinkauf, and Baumgart-Getz (2008) conducted a meta-analysis of conservation practice adoption literature which indicated that no attribute (e.g., acres, age, capital, education, farming experience, income, information, labor, networks, land tenure, attitudes, and awareness) consistently predicted nutrient management adoption . Weber and McCann (2014) found that older farmers were less likely to adopt soil testing and inhibitors; those who received no NO3 recommendations from external sources were less likely to adopt soil testing, inhibitors, and tissue testing; and those from warmer regions were less likely to adopt inhibitors. However, they found that producers outside of the Midwest were more likely to use soil testing than those in the Midwest, those who used conservation tillage were more likely to adopt plant tissue testing and NO3 inhibitors, and those who received conservation payments were more likely to adopt soil and plant tissue testing (Weber & McCann, 2014).
Some known limitations for producers in adopting nutrient best management practices include their time and resource intensiveness (Genskow 2012; McCann 2009; Stuart et al. 2014; Weld et al. 2002). Research has also shown that nutrient management plans are among the most unpopular conservation practices (Ulrich-Schad, et al., 2017).
The researchers reported in figure 1 that highest percentage of survey respondents currently used regular soil tests (85%), while fewer used application timing (65%), variable rate application technology (56%), or nutrient management plans (46%) (Ulrich-Schad, et al., 2017).The percentages of respondents using soil tests, application timing, and variable rate technology reflect only respondents who plant crops or have pasture, not all survey respondents (Ulrich-Schad, et al., 2017). The nutrient management plan percentages are only of those respondents who had livestock (Ulrich-Schad, et al., 2017). Many of those not using these specific practices, however, said that they might be willing to try them, including those who had never heard of them, had heard of them, and had used them in the past (Ulrich-Schad, et al., 2017). This research revealed that approximately 80% of those not currently using soil tests, variable rate technology, or application timing said they might be willing to try these practices (Ulrich-Schad, et al., 2017). Additionally, about 71% of those not currently using a nutrient management plan said that they might be willing to try using one in the future (Ulrich-Schad, et al., 2017).
These researchers reported that age and education did not influence adoption of these practices, possibly due to the fact that many nutrient best management practices are contracted out to crop consultants and therefore do not require personal experience or education to implement successfully (Ulrich-Schad, et al., 2017). The researchers reported that unlike other information sources, when producers said they sought information from workshops (including demonstration sites or meetings), they were more likely to use both soil tests and nutrient management plans (Ulrich-Schad, et al., 2017).
While demonstration sites are frequently used by Extension as a way to increase conservation practice adoption, limited research is available that examines whether attendance actually leads to adoption and continuance of a practice (Ulrich-Schad, et al., 2017). Rogers’ (2003) Diffusion of Innovations model does suggest the greater “observability” of a practice potentially leads to increased adoption. Other more recent studies also provide evidence that seeing practices can play a positive role in their adoption (Dromgoole, Nusser, & Ott, 2018; Boleman & Dromgoole 2007; Guerin & Guerin 1994). Additionally, those who attend such workshops are making more of a commitment to learn about a practice than those simply listening to the radio or going online, and consequently might have a greater tendency to also adopt best management practices (Ulrich-Schad, et al., 2017).
The researchers reported that while there is relatively high adoption of soil testing (85%), farmers in Indiana were less likely to use variable rate application, application timing, and nutrient management plans, and the degree to which producers adopt practices varies (Ulrich-Schad, et al., 2017). This research concluded that while there were few consistent predictors of practice adoption, the following were positive predictors of producers adopting at least two to the four practices (Ulrich-Schad, et al., 2017):
Baumgart-Getz, A., Prokopy, L.& Floress, L. (2012). Why farmers adopt best management practices in the United States: A meta-analysis of the adoption literature. Journal of Environmental Management 96(1):17-25.
Boleman, C., & Dromgoole, D. (2007). Result Demonstration: A Method That Works. 1: 87475. College Station, TX: Texas A&M AgriLife Extension
Dromgoole, D., Nusser, D., & Ott, J. (2018) Result Demonstration: A Method that Works. 08-18. College Station, TX: Texas A&M AgriLife Extension
Fishbein, M., & Ajzen, I. (2011). Predicting and changing behavior: The reasoned action approach. New York: Taylor & Francis Group.
Genskow, K. (2012). Taking stock of voluntary nutrient management: Measuring and tracking change. Journal of Soil and Water Conservation 67(1):51-58, doi:10.2489/ jswc.67.1.51.
Guerin, L., & Guerin, T. 1994. Constraints to the adoption of innovations in agricultural research and environmental management: A review. Australian Journal of Experimental Agriculture 34:549-571.
Knowler, D., & Bradshaw, B. (2007). Farmers’ adoption of conservation agriculture: A review and synthesis of recent research. Food Policy 32:25-48.
McCann, L. (2009). Transaction costs of environmental policies and returns to scale: The case of comprehensive nutrient management plans. Review of Agriculture Economics 31(3):561-573
Prokopy, L., Floress, K., Kllatthar-Weinkauf,, D. & Baumgart-Getz, B. (2008). Determinants of agricultural best management practice adoption: Evidence from the literature. Journal of Soil and Water Conservation 63(5):300-311, doi:10.2489/jswc.63.5.300
Reimer, A., Thompson, A. . Prokopy, L,Arbuckle, J., Genskow, K., Jackson-Smith, D., Lynne, G., McCann, L. ,Morton, L. & Nowak P. (2014). People, place, behavior and context: A research agenda for expanding our understanding of what motivates farmers’ conservation behaviors. Journal of Soil and Water Conservation 69(2):57A-61A, doi:10.2489/ jswc.69.2.57A.
Rogers, E. (2003). Diffusion of Innovations, 5th ed. New York: Free Press.
Stuart, D., Schewe, R. & McDermott, M.. (2014). Reducing nitrogen fertilizer application as a climate change mitigation strategy: Understanding farmer decision-making and potential barriers to change in the US. Land Use Policy 36:210-218.
Ulrich-Schad, J., Jalon, S., Babin, N., Pape, A. &Prokopy, L. (2017). How measuring and understanding agricultural producers’ adoption of nutrient best management practices. Journal of Soil and Water Conservation. 72 (5).
Weber, C., & McCann, L. (2014). Adoption of nitrogenefficient technologies by US Corn farmers. Journal of Environmental Quality 44(2):391-401.
Weld, J., Parsons, R., Beegle, D., Sharpley, A., Gburek, W. & Clouser. W. (2002). Evaluation of phosphorus-based nutrient management strategies in Pennsylvania. Journal of Soil and Water Conservation 57(6):448-454