Climate variability and change is a serious threat to the livelihoods of rural communities because they are very sensitive to such changes. This study identified farmers’ choice of and factors determining adaptation strategies to climate variability and change in Benishangul Gumuz regional state, western Ethiopia which is harshly affected by climate change stresses. Both primary and secondary data were used for the study. Primary data were collected from a randomly selected 395 sample households through interviewed using field-based questionnaires and focus group discussions. Relevant secondary data were also obtained from Benishangul Gumuz region Agriculture and Natural resource Bureau, national meteorological agency and different reports. Descriptive statistics were used to describe farmers’ adaptation strategies to climate change. Multivariate probit model was estimated to identify the factors determining households’ choice of adaptation strategies to climate change. The results of the model pointed out that the likelihood of households to adopt soil and water conservation practice, crop diversity, small scale irrigation, improved crop varieties, agrochemical applications and adjusting planting date were 64.7%, 70.4%, 65.5%, 64.2%, 63.6% and 58.9% respectively. The results also indicated that the joint likelihood of using all adaptation strategies was only 2.13% and the joint likelihood of failure to adopt all of the adaptation strategies was 2.82%. Moreover, Multivariate probit model confirmed that age, sex, education status, family size, dependency ratio, total land holding, farming experience, credit access, frequency of extension contacts, distance to the market, total livestock holding, farm income and off/non-farm income have a statistically significant impact on climate adaptation strategies. Therefore, policy makers should focus on towards supporting improved extension service, facilitating the availability of credit especially to adaptation technologies, improving farmers farm income earning opportunities, improving their literacy status, and improving their access to markets.
Published in | International Journal of Sustainable Development Research (Volume 10, Issue 2) |
DOI | 10.11648/j.ijsdr.20241002.12 |
Page(s) | 56-64 |
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), 2024. Published by Science Publishing Group |
Determinates, Adaptation Strategies, Climate Change, Multivariate Probit Model
Farmers’ perception of climate change | Response (Yes %) |
---|---|
Change in patterns, amount and intensity of rainfall | 82.3 |
Heavy storms | 54.7 |
Pest and diseases of crops | 69.7 |
High daytime temperature | 89.4 |
Change in heat and cold period | 62.1 |
Frequency of drought | 79.5 |
Flooding | 32.3 |
Adaptation Strategies | Mean | Standard Error |
---|---|---|
Mulching | 0.49 | 0.501 |
Soil conservation practices | 0.65 | 0.479 |
Planting trees | 0.27 | 0.446 |
Small scale irrigation | 0.65 | 0.476 |
Crop diversification | 0.70 | 0.457 |
Improved crop varieties | 0.64 | 0.480 |
Applications of Agrochemicals | 0.64 | 0.482 |
Crop rotations | 0.56 | 0.498 |
Adjusting planting date | 0.59 | 0.493 |
Switching to short maturing crops | 0.53 | 0.500 |
Explanatory Variables | Crop Diversity | SC Practice | SS Irrigation | Crop Rotation | AP Date | Impr Varieties |
---|---|---|---|---|---|---|
Coeff. (Std.Err) | Coeff. (Std.Err) | Coeff. (Std.Err) | Coeff. (Std.Err) | Coeff. (Std.Err) | Coeff. (Std.Err) | |
Age | 0.014 (0.008)* | -0.02 (0.008)*** | 0.001 (0.008) | 0.004 (0.008) | -0.003 (0.008) | 0.014 (0.008)* |
Sex | 0.218 (0.227) | 0.308 (0.219) | -0.022 (0.219) | 0.339 (0.222) | -0.332 (0.216) | 0.201 (0.212) |
Educ. status | -0.027 (0.026) | 0.031 (0.025) | 0.001 (0.025) | 0.026 (0.025) | 0.044 (0.026)* | -0.013 (0.025) |
Family size | -0.008 (0.019) | -0.028 (0.019) | -0.001 (0.018) | -0.048 (0.019)** | 0.022 (0.019) | -0.033 (0.019)* |
Dependency ra | -0.106 (0.062)* | -0.102 (0.059)* | -0.052 (0.058) | -0.068 (0.059) | -0.021 (0.057) | -0.047 (0.058) |
Total Land | 0.057 (0.024)** | -0.035 (0.021)* | 0.046 (0.022)** | 0.042 (0.021)** | -0.009 (0.021) | 0.022 (0.02) |
Experience | 0.016 (0.009)* | 0.017 (0.008)** | 0.022 (0.008)*** | 0.003 (0.008) | 0.001 (0.008) | -0.0004 (0.008) |
Credit access | 0.270 (0.139)* | 0.228 (0.137)* | -0.057 (0.138) | 0.068 (0.138) | 0.516 (0.139)*** | -0.143 (0.136) |
Exte. contact | -0.009 (0.071) | 0.004 (0.070) | 0.046 (0.07) | 0.005 (0.071) | 0.151 (0.071)** | -0.056 (0.070) |
Access to Mkt | -0.052 (0.026)** | 0.003 (0.026) | 0.037 (0.026) | 0.013 (0.026) | -0.024 (0.026) | 0.032 (0.026) |
Livestock | -0.017 (0.011) | 0.0004 (0.011) | -0.008 (0.011) | 0.018 (0.011)* | -0.001 (0.011) | 0.001 (0.011) |
Climate Info. | 0.054 (0.140) | -0.077 (0.139) | 0.248 (0.138)* | 0.349 (0.14)** | 0.094 (0.141) | -0.051 (0.139) |
Training acc | 0.159 (0.137) | -0.028 (0.134) | 0.088 (0.134) | -0.012 (0.135) | -0.091 (0.136) | 0.232 (0.134)* |
lnFarminco. | 0.175 (0.078)** | 0.011 (0.075) | 0.049 (0.076) | 0.067 (0.076) | -0.007 (0.076) | 0.08 (0.075) |
lnOffarminco. | -0.004 (0.034) | 0.03 (0.034) | -0.031 (0.034) | -0.014 (0.034) | -0.03 (0.036) | -0.012 (0.033) |
_cons | -2.30 (0.857)*** | 0.061 (0.813) | -1.128 (0.815) | -1.4 (0.828)* | -0.434 (0.815) | -1.247 (0.805) |
Rho2 | 0.030 | |||||
Rho3 | 0.307*** | -0.0.099 | ||||
Rho4 | -0.058 | 0.152* | -0.301*** | |||
Rho5 | -0.232*** | 0.090 | 0.022 | -0.060 | ||
Rho6 | -0.049 | 0.158* | -0.261*** | 0.434*** | 0.140* | |
Pre prob to adapt | 0.542 | 0.498 | 0.473 | 0.453 | 0.444 | 0.435 |
Joint probability (success) = 0.0164 Joint probability (failure) = 0.0292 | ||||||
Likelihood ratio test of Rhoij = 0, chi2 (15) = 82.5469, prob>chi2 = 0.0000 Draw number = 100; No of observation = 385; Wald chi2(84) = 169.11; Log likelihood= -1466.5808 |
CSA | Central Statistical Agency |
MVP | Multivariate Probit |
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APA Style
Mersha, F., Haji, J., Emana, B., Mehare, A. (2024). Choices of Adaptation Strategies to Climate Variability and Its Determinants: Evidence from Farm Households of Benishangul Gumuz Regional State, Western Ethiopia. International Journal of Sustainable Development Research, 10(2), 56-64. https://doi.org/10.11648/j.ijsdr.20241002.12
ACS Style
Mersha, F.; Haji, J.; Emana, B.; Mehare, A. Choices of Adaptation Strategies to Climate Variability and Its Determinants: Evidence from Farm Households of Benishangul Gumuz Regional State, Western Ethiopia. Int. J. Sustain. Dev. Res. 2024, 10(2), 56-64. doi: 10.11648/j.ijsdr.20241002.12
AMA Style
Mersha F, Haji J, Emana B, Mehare A. Choices of Adaptation Strategies to Climate Variability and Its Determinants: Evidence from Farm Households of Benishangul Gumuz Regional State, Western Ethiopia. Int J Sustain Dev Res. 2024;10(2):56-64. doi: 10.11648/j.ijsdr.20241002.12
@article{10.11648/j.ijsdr.20241002.12, author = {Firomsa Mersha and Jema Haji and Bezabih Emana and Abule Mehare}, title = {Choices of Adaptation Strategies to Climate Variability and Its Determinants: Evidence from Farm Households of Benishangul Gumuz Regional State, Western Ethiopia }, journal = {International Journal of Sustainable Development Research}, volume = {10}, number = {2}, pages = {56-64}, doi = {10.11648/j.ijsdr.20241002.12}, url = {https://doi.org/10.11648/j.ijsdr.20241002.12}, eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsdr.20241002.12}, abstract = {Climate variability and change is a serious threat to the livelihoods of rural communities because they are very sensitive to such changes. This study identified farmers’ choice of and factors determining adaptation strategies to climate variability and change in Benishangul Gumuz regional state, western Ethiopia which is harshly affected by climate change stresses. Both primary and secondary data were used for the study. Primary data were collected from a randomly selected 395 sample households through interviewed using field-based questionnaires and focus group discussions. Relevant secondary data were also obtained from Benishangul Gumuz region Agriculture and Natural resource Bureau, national meteorological agency and different reports. Descriptive statistics were used to describe farmers’ adaptation strategies to climate change. Multivariate probit model was estimated to identify the factors determining households’ choice of adaptation strategies to climate change. The results of the model pointed out that the likelihood of households to adopt soil and water conservation practice, crop diversity, small scale irrigation, improved crop varieties, agrochemical applications and adjusting planting date were 64.7%, 70.4%, 65.5%, 64.2%, 63.6% and 58.9% respectively. The results also indicated that the joint likelihood of using all adaptation strategies was only 2.13% and the joint likelihood of failure to adopt all of the adaptation strategies was 2.82%. Moreover, Multivariate probit model confirmed that age, sex, education status, family size, dependency ratio, total land holding, farming experience, credit access, frequency of extension contacts, distance to the market, total livestock holding, farm income and off/non-farm income have a statistically significant impact on climate adaptation strategies. Therefore, policy makers should focus on towards supporting improved extension service, facilitating the availability of credit especially to adaptation technologies, improving farmers farm income earning opportunities, improving their literacy status, and improving their access to markets. }, year = {2024} }
TY - JOUR T1 - Choices of Adaptation Strategies to Climate Variability and Its Determinants: Evidence from Farm Households of Benishangul Gumuz Regional State, Western Ethiopia AU - Firomsa Mersha AU - Jema Haji AU - Bezabih Emana AU - Abule Mehare Y1 - 2024/05/30 PY - 2024 N1 - https://doi.org/10.11648/j.ijsdr.20241002.12 DO - 10.11648/j.ijsdr.20241002.12 T2 - International Journal of Sustainable Development Research JF - International Journal of Sustainable Development Research JO - International Journal of Sustainable Development Research SP - 56 EP - 64 PB - Science Publishing Group SN - 2575-1832 UR - https://doi.org/10.11648/j.ijsdr.20241002.12 AB - Climate variability and change is a serious threat to the livelihoods of rural communities because they are very sensitive to such changes. This study identified farmers’ choice of and factors determining adaptation strategies to climate variability and change in Benishangul Gumuz regional state, western Ethiopia which is harshly affected by climate change stresses. Both primary and secondary data were used for the study. Primary data were collected from a randomly selected 395 sample households through interviewed using field-based questionnaires and focus group discussions. Relevant secondary data were also obtained from Benishangul Gumuz region Agriculture and Natural resource Bureau, national meteorological agency and different reports. Descriptive statistics were used to describe farmers’ adaptation strategies to climate change. Multivariate probit model was estimated to identify the factors determining households’ choice of adaptation strategies to climate change. The results of the model pointed out that the likelihood of households to adopt soil and water conservation practice, crop diversity, small scale irrigation, improved crop varieties, agrochemical applications and adjusting planting date were 64.7%, 70.4%, 65.5%, 64.2%, 63.6% and 58.9% respectively. The results also indicated that the joint likelihood of using all adaptation strategies was only 2.13% and the joint likelihood of failure to adopt all of the adaptation strategies was 2.82%. Moreover, Multivariate probit model confirmed that age, sex, education status, family size, dependency ratio, total land holding, farming experience, credit access, frequency of extension contacts, distance to the market, total livestock holding, farm income and off/non-farm income have a statistically significant impact on climate adaptation strategies. Therefore, policy makers should focus on towards supporting improved extension service, facilitating the availability of credit especially to adaptation technologies, improving farmers farm income earning opportunities, improving their literacy status, and improving their access to markets. VL - 10 IS - 2 ER -