Factor Affecting Adoption of IPM Technology; an Example from Banke and Surkhet District of Nepal

Arjun Khanal; Punya Prasad Regmi; Gopal Bahadur KC; Dilli Bahadur KC; Kishor Chandra Dahal

doi:doi:10.11648/j.ijae.20200506.19

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Factor Affecting Adoption of IPM Technology; an Example from Banke and Surkhet District of Nepal

Arjun Khanal, Punya Prasad Regmi, Gopal Bahadur KC, Dilli Bahadur KC, Kishor Chandra Dahal

Published in International Journal of Agricultural Economics (Volume 5, Issue 6)

Received: 29 October 2020 Accepted: 9 November 2020 Published: 23 November 2020

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Abstract

This study evaluates the factor affecting adoption of IPM technology in Banke and Surkhet district of Nepal. An adoption analysis is necessary for describing and measuring the adoption of IPM technologies, which can provide important policy information that can lead to improvement of farmers’ lives. The dependent variable in the following adoption analysis can take four values 1, 2, 3 and 4, indicating different levels of adoption. Due to the ordered nature of the dependent variable the model used was an ordered probit model. The determinants of adoption included in the present model belong in three main categories: socio-demographic, economic, and institutional characteristics. Five variables were statistically significant at 1% level for practicing IPM technology, they were; experience, training, MPC, mass media, and farmer field school. Two variables were statistically significant at 5% level for practicing IPM technology, they were; awareness of pesticides alternatives and field day. One variable age is statistically significant at 10% level for practicing IPM technology. Seven others variables namely gender, total family member, education, farm area, extension agent, credit and visit were statistically non significant. The sign of the coefficient in the coefficient columns shows the type of impact, positive or negative, by the particular variable.

Published in	International Journal of Agricultural Economics (Volume 5, Issue 6)
DOI	10.11648/j.ijae.20200506.19
Page(s)	304-312
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2020. Published by Science Publishing Group

Keywords

IPM Technology, Factor Affecting, Adoption and Market Planning Committee

References

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[2]	Feder G., R. E. Just, and D. Zilberman. 1985. "Adoption of Agricultural Innovations in Developing Countries: A Survey." Economic Development and Cultural Change 33(2): 255-298.
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[9]	Bonabana-Wabbi, Jackline. Assessing Factors Affecting Adoption of Agricultural Technologies: The Case of Integrated Pest Management (IPM) in Kumi District, Eastern Uganda. MS thesis, Virginia Polytechnic Institute and State University, 2002.
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[11]	De Souza-Filho, H. M., T. Young, and M. P. Burton. 1999. “Factors Influencing the Adoption of Sustainable Agricultural Technologies Evidence from the State of Espirito Santo, Brazil.” Technological Forecasting and Social Change 60:97-112.
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[15]	Adesina, A. A., D. Mbila, G. B. Nkamleu, and D. Endamana. 2000. "Econometric analysis of the determinants of adoption of alley farming by farmers in the forest zone of southwest Cameroon." Agriculture, Ecosystems & Environment 80(3): 255-265.
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[18]	Nhemachena, C. and R. Hassan. 2007. Micro level analysis of farmers’ adaptation to climate change in Southern Africa. IFPRI Discussion Paper No. 00714. International Food Policy Research Institute, Washington D.C.
[19]	Gbetibouo, G. A. 2009. Understanding farmers’ perceptions and adaptations to climate change and variability, the case of the Limpopo Basin, South Africa. IFPRI Discussion Paper No. 00849. Environment and Production Technology Division, International Food Policy Research Institute.
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APA Style

Arjun Khanal, Punya Prasad Regmi, Gopal Bahadur KC, Dilli Bahadur KC, Kishor Chandra Dahal. (2020). Factor Affecting Adoption of IPM Technology; an Example from Banke and Surkhet District of Nepal. International Journal of Agricultural Economics, 5(6), 304-312. https://doi.org/10.11648/j.ijae.20200506.19

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ACS Style

Arjun Khanal; Punya Prasad Regmi; Gopal Bahadur KC; Dilli Bahadur KC; Kishor Chandra Dahal. Factor Affecting Adoption of IPM Technology; an Example from Banke and Surkhet District of Nepal. Int. J. Agric. Econ. 2020, 5(6), 304-312. doi: 10.11648/j.ijae.20200506.19

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AMA Style

Arjun Khanal, Punya Prasad Regmi, Gopal Bahadur KC, Dilli Bahadur KC, Kishor Chandra Dahal. Factor Affecting Adoption of IPM Technology; an Example from Banke and Surkhet District of Nepal. Int J Agric Econ. 2020;5(6):304-312. doi: 10.11648/j.ijae.20200506.19

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@article{10.11648/j.ijae.20200506.19,
  author = {Arjun Khanal and Punya Prasad Regmi and Gopal Bahadur KC and Dilli Bahadur KC and Kishor Chandra Dahal},
  title = {Factor Affecting Adoption of IPM Technology; an Example from Banke and Surkhet District of Nepal},
  journal = {International Journal of Agricultural Economics},
  volume = {5},
  number = {6},
  pages = {304-312},
  doi = {10.11648/j.ijae.20200506.19},
  url = {https://doi.org/10.11648/j.ijae.20200506.19},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijae.20200506.19},
  abstract = {This study evaluates the factor affecting adoption of IPM technology in Banke and Surkhet district of Nepal. An adoption analysis is necessary for describing and measuring the adoption of IPM technologies, which can provide important policy information that can lead to improvement of farmers’ lives. The dependent variable in the following adoption analysis can take four values 1, 2, 3 and 4, indicating different levels of adoption. Due to the ordered nature of the dependent variable the model used was an ordered probit model. The determinants of adoption included in the present model belong in three main categories: socio-demographic, economic, and institutional characteristics. Five variables were statistically significant at 1% level for practicing IPM technology, they were; experience, training, MPC, mass media, and farmer field school. Two variables were statistically significant at 5% level for practicing IPM technology, they were; awareness of pesticides alternatives and field day. One variable age is statistically significant at 10% level for practicing IPM technology. Seven others variables namely gender, total family member, education, farm area, extension agent, credit and visit were statistically non significant. The sign of the coefficient in the coefficient columns shows the type of impact, positive or negative, by the particular variable.},
 year = {2020}
}

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TY  - JOUR
T1  - Factor Affecting Adoption of IPM Technology; an Example from Banke and Surkhet District of Nepal
AU  - Arjun Khanal
AU  - Punya Prasad Regmi
AU  - Gopal Bahadur KC
AU  - Dilli Bahadur KC
AU  - Kishor Chandra Dahal
Y1  - 2020/11/23
PY  - 2020
N1  - https://doi.org/10.11648/j.ijae.20200506.19
DO  - 10.11648/j.ijae.20200506.19
T2  - International Journal of Agricultural Economics
JF  - International Journal of Agricultural Economics
JO  - International Journal of Agricultural Economics
SP  - 304
EP  - 312
PB  - Science Publishing Group
SN  - 2575-3843
UR  - https://doi.org/10.11648/j.ijae.20200506.19
AB  - This study evaluates the factor affecting adoption of IPM technology in Banke and Surkhet district of Nepal. An adoption analysis is necessary for describing and measuring the adoption of IPM technologies, which can provide important policy information that can lead to improvement of farmers’ lives. The dependent variable in the following adoption analysis can take four values 1, 2, 3 and 4, indicating different levels of adoption. Due to the ordered nature of the dependent variable the model used was an ordered probit model. The determinants of adoption included in the present model belong in three main categories: socio-demographic, economic, and institutional characteristics. Five variables were statistically significant at 1% level for practicing IPM technology, they were; experience, training, MPC, mass media, and farmer field school. Two variables were statistically significant at 5% level for practicing IPM technology, they were; awareness of pesticides alternatives and field day. One variable age is statistically significant at 10% level for practicing IPM technology. Seven others variables namely gender, total family member, education, farm area, extension agent, credit and visit were statistically non significant. The sign of the coefficient in the coefficient columns shows the type of impact, positive or negative, by the particular variable.
VL  - 5
IS  - 6
ER  -

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Author Information

Arjun Khanal

Institute of Agriculture and Animal Science, Tribhuwan University, Kirtipur, Kathmandu
Punya Prasad Regmi

Agriculture and Forestry University, Rampur, Chitwan
Gopal Bahadur KC

Department of Plant Pathology, Institute of Agriculture and Animal Science, Kirtipur, Kathmandu
Dilli Bahadur KC

International Maize and Wheat Improvement Center (CIMMYT), Lalitpur, Nepal
Kishor Chandra Dahal

Department of Horticulture, Institute of Agriculture and Animal Science, Tribhuwan University, Kirtipur, Kathmandu

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APA Style

Arjun Khanal, Punya Prasad Regmi, Gopal Bahadur KC, Dilli Bahadur KC, Kishor Chandra Dahal. (2020). Factor Affecting Adoption of IPM Technology; an Example from Banke and Surkhet District of Nepal. International Journal of Agricultural Economics, 5(6), 304-312. https://doi.org/10.11648/j.ijae.20200506.19

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ACS Style

Arjun Khanal; Punya Prasad Regmi; Gopal Bahadur KC; Dilli Bahadur KC; Kishor Chandra Dahal. Factor Affecting Adoption of IPM Technology; an Example from Banke and Surkhet District of Nepal. Int. J. Agric. Econ. 2020, 5(6), 304-312. doi: 10.11648/j.ijae.20200506.19

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AMA Style

Arjun Khanal, Punya Prasad Regmi, Gopal Bahadur KC, Dilli Bahadur KC, Kishor Chandra Dahal. Factor Affecting Adoption of IPM Technology; an Example from Banke and Surkhet District of Nepal. Int J Agric Econ. 2020;5(6):304-312. doi: 10.11648/j.ijae.20200506.19

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@article{10.11648/j.ijae.20200506.19,
  author = {Arjun Khanal and Punya Prasad Regmi and Gopal Bahadur KC and Dilli Bahadur KC and Kishor Chandra Dahal},
  title = {Factor Affecting Adoption of IPM Technology; an Example from Banke and Surkhet District of Nepal},
  journal = {International Journal of Agricultural Economics},
  volume = {5},
  number = {6},
  pages = {304-312},
  doi = {10.11648/j.ijae.20200506.19},
  url = {https://doi.org/10.11648/j.ijae.20200506.19},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijae.20200506.19},
  abstract = {This study evaluates the factor affecting adoption of IPM technology in Banke and Surkhet district of Nepal. An adoption analysis is necessary for describing and measuring the adoption of IPM technologies, which can provide important policy information that can lead to improvement of farmers’ lives. The dependent variable in the following adoption analysis can take four values 1, 2, 3 and 4, indicating different levels of adoption. Due to the ordered nature of the dependent variable the model used was an ordered probit model. The determinants of adoption included in the present model belong in three main categories: socio-demographic, economic, and institutional characteristics. Five variables were statistically significant at 1% level for practicing IPM technology, they were; experience, training, MPC, mass media, and farmer field school. Two variables were statistically significant at 5% level for practicing IPM technology, they were; awareness of pesticides alternatives and field day. One variable age is statistically significant at 10% level for practicing IPM technology. Seven others variables namely gender, total family member, education, farm area, extension agent, credit and visit were statistically non significant. The sign of the coefficient in the coefficient columns shows the type of impact, positive or negative, by the particular variable.},
 year = {2020}
}

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TY  - JOUR
T1  - Factor Affecting Adoption of IPM Technology; an Example from Banke and Surkhet District of Nepal
AU  - Arjun Khanal
AU  - Punya Prasad Regmi
AU  - Gopal Bahadur KC
AU  - Dilli Bahadur KC
AU  - Kishor Chandra Dahal
Y1  - 2020/11/23
PY  - 2020
N1  - https://doi.org/10.11648/j.ijae.20200506.19
DO  - 10.11648/j.ijae.20200506.19
T2  - International Journal of Agricultural Economics
JF  - International Journal of Agricultural Economics
JO  - International Journal of Agricultural Economics
SP  - 304
EP  - 312
PB  - Science Publishing Group
SN  - 2575-3843
UR  - https://doi.org/10.11648/j.ijae.20200506.19
AB  - This study evaluates the factor affecting adoption of IPM technology in Banke and Surkhet district of Nepal. An adoption analysis is necessary for describing and measuring the adoption of IPM technologies, which can provide important policy information that can lead to improvement of farmers’ lives. The dependent variable in the following adoption analysis can take four values 1, 2, 3 and 4, indicating different levels of adoption. Due to the ordered nature of the dependent variable the model used was an ordered probit model. The determinants of adoption included in the present model belong in three main categories: socio-demographic, economic, and institutional characteristics. Five variables were statistically significant at 1% level for practicing IPM technology, they were; experience, training, MPC, mass media, and farmer field school. Two variables were statistically significant at 5% level for practicing IPM technology, they were; awareness of pesticides alternatives and field day. One variable age is statistically significant at 10% level for practicing IPM technology. Seven others variables namely gender, total family member, education, farm area, extension agent, credit and visit were statistically non significant. The sign of the coefficient in the coefficient columns shows the type of impact, positive or negative, by the particular variable.
VL  - 5
IS  - 6
ER  -

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