A paper presented during Kentucky Academy of Science annual meeting at Murray State University, Kentucky.

1. Bijesh Mishra*, Buddhi Gyawali
Kentucky Academy of Science
103rd Annual Meeting
Murray State University
Murray, Kentucky
November 3-4, 2017
*bijesh.mishra@kysu.edu

2. Outline of Presentation
• Introduction and Literature Review
• Objectives
• Hypothesis
• Methods
• Results
• Conclusions
• References
• Acknowledgements

3. • Agriculture has been an
important part of Kentucky
economy.
• $45.6 billion economic impact
in 2013. (Bollinger et al., 2015;
Burney & Davis, 2015).

4. Introduction and Literature Review
• US Congress (1990) defined sustainable agriculture as an integrated system
of plant and animal production practices... that will, over the long-term,
satisfy human food and fiber needs, enhance environmental quality…,
sustain economic viability…, and enhance quality of life.
• The interest in sustainable agriculture and food system can be traced back to
1950s-60s environmental degradation (Pretty, 2008) and gained prominence
after the Brundland Report in 1987 (Velten et al., 2015).

5. • Many researchers identified important roles of socioeconomic,
demographics, farm attributes, awareness, knowledge, skills, etc. in
predicting the adoption of Sustainable Agriculture Practices (SAPs) (Kabii &
Horwitz, 2006; Prokopy et al., 2008; Kornegay et al., 2010; Bertgold et al.,
2012; Carlisle, 2016)
• In contrast, Prokopy et al., (2008) found socioeconomic, demographic,
attitude variables were not significant in major studies conducted in the US
between 1980-2005.
• Adoption of SAPs, its predictors, and adoption barriers in Kentucky are not
well-understood.
Introduction and Literature Review

6. Statement of Problem
6
Designing locally accepted alternative and viable food systems requires
understanding of:
• Factors affecting the adoption of SAPs in relation to demographic and
socioeconomic conditions, farm attributes, knowledge, attitudes and
their relationships with SAPs; and
• Adoption barriers of SAPs.

7. Objectives
The specific objectives of this research are:
• To identify predictors of adoption of SAPs using farm attributes, farmer
attitudes, socioeconomic, and demographic factors.
• To evaluate barriers to adoption of SAPs among Kentucky farmers.
7

8. Hypothesis
8
• The adoption of SAPs among Kentucky farmers has a significant
relationship with farm attributes, farmer attitudes, knowledge,
socioeconomic, and demographic factors.

9. Method and Materials
• Focus group discussion
• Survey Pretested, feedbacks addressed and Mailed Mail survey
• Double-stratified sampling method for survey distribution
• Post-strata weight was applied before analyzing
• Mapped Variables using KY-Shape file.
• Negative Binomial Regression (with and without agriculture districts)
• Factor Analysis
9

10. Survey Responders by Ag-District
Agriculture District

11. Variables Variable Name Types
Relation
with DV
SAPs Number of SAPs adopted by Farmers Count DV
Crops
Row Crop Farmers
Yes = 1; Otherwise = 0
Binary +Ve
Veggies
Vegetable Growing Farmers
Yes = 1; Otherwise = 0
Binary +Ve
Livestock
Livestock Farmers
Yes = 1; Otherwise = 0
Binary -Ve
Irrigation
Irrigation Facility in Farm
Yes = 1; Otherwise = 0
Binary +Ve
Diverse
In Favor of Diversifying Farm
Yes = 1; Otherwise = 0
Binary +Ve
Sole Proprietorship
Farm with Sole Proprietorship
Yes = 1, Otherwise = 0
Binary +Ve
Off Farm
Working off Farm
Yes = 1; Otherwise = 0
Binary +Ve
Age Age (Years) Continuous -Ve
TBP
Participated
Yes = 1; Otherwise = 0
Binary +Ve
Variables Descriptions
DV = Dependent variable, +Ve & -Ve = positive and negative relationship with
dependent variable, respectively.

12. Variables Variable Name Types
Relation
with DV
College Degree
Formal Education: College Degree or above
Yes = 1; Otherwise= 0 Binary
+Ve
Land Total Land Operated (Acres) Continuous +Ve
Happy
Happy attitude (A reason for not adopting SAPs)
Yes = 1; Otherwise = 0 Binary
-Ve
Implementation
Difficulty
Perceived difficult of implementation of
practices
Yes = 1; Otherwise = 0
Binary
-Ve
Inadequate Knowledge
A reason for not adopting SAP
Yes = 1; Otherwise = 0
Binary -Ve
The following regional dummy variables represents NASS Agricultural Districts in Kentucky.
AgDIST_1 Agricultural District 10 (Western) Binary N/A
AgDIST_2 Agricultural District 20 (Western) Binary N/A
AgDIST_3 Agricultural District 30 (Central) Binary N/A
AgDIST_4 Agricultural District 40 (Central) Binary N/A
AgDIST_5 Agricultural District 50 (Central) Binary N/A
AgDIST_6 Agricultural District 60 (Eatern) Binary N/A
Variables Descriptions (Cont’d.)
DV = Dependent variable, +Ve & -Ve = positive and negative relationship with
dependent variable, respectively.

13. Distribution of SAPs among Ag-Districts

14. Distribution of SAPs among Ag-Districts

15. Variables β Std.
Error
Wald Chi-
Square
Sig. Exp(β)
95% Wald Confidence
[Exp(β)]
Lower Upper
Constant -0.422 0.397 1.130 0.288 0.656 0.301 1.427
Crops*** 0.830 0.115 52.041 0.000 2.293 1.830 2.874
Veggies 0.038 0.123 0.096 0.756 1.039 0.816 1.324
Livestock 0.014 0.135 0.011 0.918 1.014 0.779 1.320
Irrigation*** 0.921 0.169 29.601 0.000 2.512 1.803 3.500
Diverse*** 0.827 0.098 71.490 0.000 2.287 1.888 2.771
Sole Proprietorship -0.023 0.108 0.045 0.831 0.977 0.791 1.208
Off Farm*** 0.382 0.113 11.361 0.001 1.466 1.174 1.831
Age (Years) -0.002 0.005 0.124 0.724 0.998 0.989 1.008
TBP* 0.180 0.101 3.165 0.075 1.197 0.982 1.459
College Degree*** 0.755 0.125 36.218 0.000 2.127 1.664 2.720
Land** 0.000 0.000 4.734 0.030 1.000 1.000 1.001
Happy 0.051 0.093 0.304 0.581 1.053 0.877 1.263
Implementation Difficulty*** -1.534 0.176 75.832 0.000 4.636 3.283 6.548
Inadequate Knowledge*** -0.842 0.148 32.175 0.000 0.431 0.322 0.576
Result: NBR Model (W/O Ag. Districts)
Single, double and triple asterisks (*) denotes significance of variables at 90%, 95% and 99% confidence
intervals respectively; YELLOW highlighted variables have NEGATIVE signs in the model.

16. Variables β Std.
Error
Wald Chi-
Square Sig.
Exp(β)
95% Wald Confidence
[Exp(β)]
Lower Upper
Constant 0.024 0.423 0.003 0.955 1.024 0.447 2.347
Crops*** 0.830 0.121 47.052 0.000 2.294 1.809 2.908
Veggies*** 0.413 0.136 9.262 0.002 1.511 1.158 1.971
Livestock 0.228 0.143 2.553 0.110 1.257 0.950 1.663
Irrigation*** 0.629 0.171 13.589 0.000 1.876 1.343 2.621
Diverse*** 0.728 0.100 53.242 0.000 2.072 1.704 2.519
Sole Proprietorship -0.084 0.116 0.525 0.469 0.920 0.733 1.154
Off Farm 0.063 0.120 0.278 0.598 1.065 0.843 1.346
Age (Years)** -0.010 0.005 3.892 0.049 0.990 0.980 1.000
TBP** 0.251 0.105 5.739 0.017 1.286 1.047 1.579
College Degree*** 0.740 0.136 29.603 0.000 2.097 1.606 2.738
Land 0.000 0.000 2.611 0.106 1.000 1.000 1.000
Happy 0.004 0.102 0.001 0.972 1.004 0.822 1.226
Implementation Difficulty*** -1.588 0.180 77.703 0.000 4.892 3.437 6.964
Inadequate Knowledge*** -0.631 0.158 15.962 0.000 0.532 0.391 0.725
AgDist_1 0.225 0.194 1.346 0.246 1.253 0.856 1.833
AgDist_2*** 0.855 0.163 27.525 0.000 2.352 1.709 3.237
AgDist_3** -0.392 0.161 5.918 0.015 0.676 0.493 0.927
AgDist_4*** 0.565 0.159 12.642 0.000 1.760 1.289 2.403
AgDist_5** -0.402 0.166 5.867 0.015 0.669 0.483 0.926
Result: NBR Model (With Ag. Districts)
Single, double and triple asterisks (*) denotes significance of variables at 90%, 95% and 99% confidence
intervals respectively; YELLOW highlighted variables have NEGATIVE signs in the model.

17. Distribution of SAPs Adoption Barriers

18. 35
12 8 8 7 4 3 7
99
0
20
40
60
80
100
120
Inadequate
Knowledge
PerceivedDifficulty
ofImplementation
LackofAdequate
Marketfor
AlternativeProduct
NegativeAttitude
AboutTechnologies
LackofAppropriate
Technologies
PressuretoIncrease
Productivity
LackofConsumer
Acceptancefor
AlternativeProduct
Others
HappyWithWhatI
amDoing
NumberofFarmers
Adoption Barriers of Sustainable Agriculture Practices
5.22% 3.48%
Result: Adoption Barriers of SAPs
15.22%
3.48% 3.04% 1.74% 1.30% 3.04%
43.04%
Revenue/Market
(Factor loading > 0.8)
Knowledge (Factor
loading > 0.6)

19. Conclusions
• Row crop growers, farm with irrigation facilities, farmers in favor of farm
diversification, and farmers an education above a college degree are
significant and positive predictors of the adoption of SAPs.
• Age has negative relationship with the adoption of SAPs; young farmers are
important for the sustainability of agriculture in Kentucky.
• Knowledge and market factors are important to increase adoption of SAPs,
which emphasize the importance of extension outreach activities and
networking among farmers.
• The adoption of sustainable agriculture also varies significantly between
agriculture districts. Thus, it is important to consider spatial component in
research to understand the socioeconomic aspects of agriculture of
Kentucky.
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20. ReferencesBergtold, J. S., Duffy, P. A., Hite, D., & Raper, R. L. (2012). Demographic and Management Factors Affecting the Adoption and Perceived Yield Benefit of Winter Cover
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Carlisle, L. (2016). Factors influencing farmer adoption of soil health practices in the United States: A narrative review. Agroecology and Sustainable Food Systems,
40(6), 583-613. doi:10.1080/21683565.2016.1156596
Coxe, S., West, S. G., & Aiken, L. S. (2009). The Analysis of Count Data: A Gentle Introduction to Poisson Regression and Its Alternatives. Journal of Personality
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terms-1#toc2. ISSN 1052-5368
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Conservation, 33(01), 11-20. doi:10.1017/s0376892906002761
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doi:10.1016/j.foodpol.2006.01.003
Kornegay, J.L., Harwood, R.R., Batie, S.S., Bucks, D., Flora, C.B., Hanson, J., Jackson-Smith, D., Jury, W., Meyer, D., Reganold, J.P., Schumacher, A. Jr., Sehmsdorf, H.,
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21. Acknowledgements
• Grant: USDA/NIFA- Farm Diversification for Strengthening of Small Farms in
KY- Award # 2014-6800621865.
• Reviewers, Survey Participants.
• College of Agriculture, Food Science and Sustainable Systems, Kentucky State
University.
• USDA/NASS.
Disclaimer: Summaries were derived using data collected in the 2014 Kentucky State University Economic Survey by the National Agriculture
Statistics Service, United States Department of Agriculture (NASS). Any interpretations and conclusion derived from the data not necessarily
represents NASS views.
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22. Thank You!!!