Examining complementary relationships among climate change adaptation practices of rice producers in Chitwan, Nepal

Figures & data

Table 1. Climate change perceptions of rice producers in Chitwan District in the past 10 years (2009–2018).

Climate change perceptions	% of respondents who perceived climate change in the past 10 years (2009–2018)
Overall change in climate patterns	76.6%
High summer temperature	86.9%
High winter temperature	61.9%
Intensity of rainfall	80.8%
Floods	29.1%
Dry spells	71.4%
Weather unpredictability	99.6%
Hailstorms	97.8%
Late onset of monsoon rain	86.2%

Source: Regmi (2020).

Table 2. Comparison of institutional factors – Extension, training and information (adapted from Singh, 1997).

	Extension	Training	Information
Purpose and content	Needs assessment, identification of an agricultural problem or seeking information	Transfer of agricultural technology, new methods, innovative solutions, skills and techniques	Easy to understand agricultural and weather information that can be rapidly distributed to a wider audience
Number of contacts	Individual contact method or face-to-face with the producer	Group contact method with a group of producers (20–25 or more)	Mass contact method to provide information quickly
Instructor/Author	Extension agent	Extension agent or subject-matter expert	Extension agent or Agricultural Graduate Officer
Location	Farm or Extension office	Extension office, Agricultural Knowledge Center, University, farm, market or a centralized location where producers can meet.	Information mailed to households; transmitted via Radio, Television; or posted on internet
Methods of instruction	Farm visits, Extension office visits and phone calls	Method or result demonstrations, in-service trainings, workshops, conferences, field trips and discussion meetings.	Factsheets, bulletins, brochures, publications and progress reports. Television/Radio or internet can provide timely information on extreme weather events or advisories.
Timeframe	1 d	2–3 days	Immediate or less than 24 h
Cost	Medium	High	Low

Table 3. Durbin–Wu–Hausman test of endogeneity for variety, irrigation and DSR.

	Variety	Irrigation	DSR
Wu–Hausman statistics	1.19671	1.76036	1.78805
p-value	p = 0.3110	p = 0.1545	p = 0.1492

Figure 1. Study area of three villages in Chitwan district, which is located in the south-central Plains (Terai) region of Nepal.

Table 4. Descriptions of variables used in the multivariate probit model.

Variable	Descriptions	Mean (n = 359)	Standard deviation
A. Dependent variables
Variety	1, if adoption of drought tolerant and short-duration rice varieties, 0 otherwise	0.64	0.47
Irrigation	1, if producer invested in improved irrigation, 0 otherwise	0.60	0.48
Direct-seeded rice (DSR)	1, if adoption of the practice, 0 otherwise	0.15	0.36
B. Explanatory variables
a. Household and socio-economic characteristics
Gender	1, if head of the household is female, 0 otherwise	0.20	0.40
Dependency ratio	Household dependency ratio (Ratio of number of dependent family members to number of economically active family members aged 16–60 years)	0.49	0.65
Out-migration	1, if any of household member migrated to another country for employment, 0 otherwise	0.45	0.49
Education	Producers’ formal education years	7.92	4.12
Farming years	Producers’ years of farming experience	26.01	10.81
Income	Annual household Income (USD)	2090.9	1472.01
Membership status	1, if any of household member is a member of farmers’ organizations (farmers’ group or cooperatives), 0 otherwise	0.68	0.46
b. Farm characteristics
Rice plots	Number of plots under rice cultivation	1.65	0.79
Rice sold	1, if sold rice in the market in addition to self-consumption, 0 otherwise	0.78	0.41
Rice area	Hectares of rice area	0.46	0.30
c. Climate-related factors
Climate	1, if perceived changes in local weather patterns over the past 10 years, 0 otherwise	0.76	0.42
d. Institutional factors
Extension	1, if producer contacted Extension agents in the past year, 0 otherwise	0.54	0.50
Training	1, if producer received agricultural training in the past year, 0 otherwise	0.44	0.48
Information	1, if producer received agricultural and weather information through TV/FM radio, weather applications on mobile phone, text-messages and publications, 0 otherwise	0.54	0.49

Figure 2. Conceptual model of triangulation and complementarity between climate change adaptations and institutional factors.

Figure 3. Average annual temperature trend of Nepal (1987–2016). Source: World Bank Group (2020).

Figure 4. Average annual precipitation trend of Nepal (1987–2016). Source: World Bank Group (2020).

Figure 5. Average annual temperature at Rampur, Chitwan (2009–2018). Source: Department of Hydrology and Meteorology, 2019, Kathmandu, Nepal.

Figure 6. Maximum, minimum and average seasonal temperature (June–October) at Rampur, Chitwan (2009–2018). Source: Department of Hydrology and Meteorology, 2019, Kathmandu, Nepal.

Figure 7. Annual and seasonal rainfall (June–October) at Rampur, Chitwan (2009–2018). Source: Department of Hydrology and Meteorology, 2019, Kathmandu, Nepal.

Table 5. Empirical results of the multivariate probit model.

Variable	Variety		Improved irrigation		Direct-seeded rice
	Coeff.	M.E.	Coeff.	M.E.	Coeff.	M.E.
a. Household and socio-economic characteristics
Gender	−0.118	−2.69%	−0.251	−6.12%	−0.307	−4.87%
	(0.284)		(0.192)		(0.282)
Dependency ratio	−0.068	−1.57%	−0.147	−3.57%	0.236*	3.74%*
	(0.147)		(0.107)		(0.127)
Out-migration	−0.326*	−7.43%*	−0.104	−2.54%	0.187	2.98%
	(0.190)		(0.171)		(0.186)
Education year	0.163***	3.71%***	0.113***	2.75%***	0.036	0.57%
	(0.033)		(0.027)		(0.031)
Farming year	0.013	0.31%	0.020**	0.49%**	−0.022**	−0.35%**
	(0.009)		(0.009)		(0.009)
Income	0.000	0.02%	0.001**	0.03%**	0.001*	0.02%*
	(0.001)		(0.000)		(0.000)
Membership	−0.392	8.92%	0.619***	15.05%***	0.265	4.20%
	(0.252)		(0.240)		(0.356)
b. Farm characteristics
Rice plot	0.223*	5.09%*	0.023	0.57%	−0.048	−0.76%
	(0.124)		(0.140)		(0.131)
Rice sold	0.325	7.39%	0.450*	10.94%*	−0.386	−6.12%
	(0.237)		(0.245)		(0.304)
Rice area	−0.708	−16.11%	−0.388	−9.44%	0.704**	10.16%**
	(0.433)		(0.332)		(0.334)
c. Climate-related factors
Climate	0.669**	15.20%**	0.699***	17.00%***	0.629*	9.96%*
	(0.272)		(0.243)		(0.418)
d. Institutional factors
Extension	0.685***	15.58%***	0.385**	9.37%**	0.383	6.08%
	(0.206)		(0.190)		(0.233)
Training	0.578**	13.14%**	0.360*	8.77%*	0.167	2.64%
	(0.235)		(0.199)		(0.204)
Information	0.412*	9.37%*	−0.052	−1.27%	0.653**	10.35%**
	(0.224)		(0.260)		(0.284)
Constant	2.915***		−2.713***		−2.733***
	(0.478)		(0.450)		(0.503)

Log-likelihood ratio ${\chi }^2$(24) = 319.010; Prob >= 0.000.

Number of observations = 359.

Log-likelihood = −330.690.

***, ** and * refer to the significance at 1%, 5% and 10% levels, respectively, numbers in parenthesis indicate the robust standard error, Coeff. denotes parameter coefficients and M.E. denotes marginal effects.

Table 6. Predicted likelihood of adoption of multiple climate change adaptation practices by hypothetically increasing institutional factors and pairwise correlation coefficients between the predicted likelihood.

	Predicted likelihood of adoption of multiple climate change adaptation practices			Pairwise correlation coefficients between the predicted likelihood
	Variety	Irrigation	DSR	Variety & Irrigation	Variety & DSR	Irrigation & DSR
Baseline	63.93%	60.45%	15.73%	–	–	–
10% increase in Extension service	65.31%	61.34%	16.23%	0.955***	0.529***	0.631***
10% increase in training related to farming practices	65.09%	61.33%	16.97%	0.942***	0.469***	0.656***
10% increase in farming practices and weather-related information	64.77%	60.33%	16.34%	–	0.602***	–

Baseline refers to the predicted likelihood of climate change adaptation practices based on the results of the three equations of the multivariate probit model.

***, ** and * refers to the significance at 1%, 5% and 10% levels, respectively.

Table

	Predicted likelihood of adoption of multiple climate change adaptation practices			Pairwise correlation coefficients between the predicted likelihood
	Variety	Irrigation	DSR	Variety & Irrigation	Variety & DSR	Irrigation & DSR
A. 20% Increase case
Baseline	63.93%	60.45%	15.73%
20% increase in Extension service	66.83%	62.26%	16.66%	0.958***	0.521***	0.64***
20% increase in training related to farming practices	66.31%	62.19%	16.21%	0.950***	0.467***	0.604***
20% increase in farming practices and weather-related information	65.59%	60.23%	16.92%		0.595***
B. 30% increase case
Baseline	63.93%	60.45%	15.73%
30% increase in Extension service	68.16%	63.07%	17.10%	0.948***	0.531***	0.658***
30% increase in training related to farming practices	67.48%	63.04%	16.46%	0.937***	0.448***	0.585***
30% increase in farming practices and weather-related information	66.40%	60.10%	17.50%		0.571***
C. 40% increase case
Baseline	63.93%	60.45%	15.73%
40% increase in Extension service	69.54%	63.98%	17.50%	0.947***	0.483***	0.609***
40% increase in training related to farming practices	68.50%	63.80%	16.70%	0.934***	0.394***	0.532***
40% increase in farming practices and weather-related information	67.30%	60.00%	18.20%		0.536***

Baseline refers to the predicted likelihood of climate change adaptation practices based on the results of the multivariate probit model.

***, ** and * refers to the significance at 1%, 5% and 10% levels, respectively.

Table A1. Correlation matrix of institutional factors.

	Extension	Training	Information
Extension	1
Training	0.486	1
Information	0.489	0.448	1

Table A2. Variation inflation factors for variables.

Variables	VIF	1/VIF
Gender	1.42	0.702261
Dependency ratio	1.69	0.592206
Out-migration	1.89	0.529518
Education	7.19	0.139004
Farming years	5.44	0.183856
Income	3.75	0.266948
Membership	6.33	0.158096
Rice plots	5.8	0.172548
Rice sold	6.14	0.162924
Rice area	4.64	0.215589
Climate	6.69	0.14948
Extension	3.41	0.293179
Training	2.7	0.37052
Information	4.65	0.215014
Mean VIF	4.41

Table A3. Conditional index values for variables.

Condition number using scaled variables = 9.48
Condition indexes and variance-decomposition proportions
SN	Condition index	Gender	Dependancy ratio	Out-migration	Educationyears	Farming year	Income	Rice plot	Rice sold	Rice area	Membership	Climate	Extension	Training	Information
1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
2	2.74	0.25	0.11	0.02	0	0	0	0	0	0	0	0	0.01	0.02	0.01
3	4.01	0.14	0.09	0.41	0	0	0.01	0	0	0.01	0	0	0.05	0.08	0.01
4	4.08	0.44	0.42	0.08	0	0	0	0	0	0.02	0	0	0	0.03	0
5	4.48	0.07	0.21	0.34	0	0	0.02	0.02	0.01	0.07	0	0	0.02	0.14	0
6	5.46	0.02	0.07	0.01	0.03	0.06	0	0.04	0	0.06	0.06	0.03	0.02	0.27	0.12
7	6.09	0	0.01	0.01	0.01	0	0.32	0	0.02	0.01	0.03	0.01	0.49	0.21	0
8	6.27	0	0.03	0.04	0	0	0.56	0	0.02	0.04	0	0.02	0.33	0.12	0
9	7.11	0	0.05	0.06	0.01	0.43	0.02	0.01	0	0.22	0	0.14	0.01	0.09	0.08
10	7.6	0.01	0	0.01	0	0.05	0.03	0.27	0.24	0.01	0.09	0	0.01	0.01	0.41
11	7.99	0.01	0	0	0.23	0.12	0	0.11	0.28	0	0.03	0.03	0.02	0.01	0.33
12	8.55	0.04	0	0.01	0.01	0.01	0.02	0.39	0.14	0.49	0.15	0.15	0.01	0	0.02
13	9	0.02	0	0	0.08	0.23	0.01	0.04	0.28	0.05	0.6	0.09	0	0.02	0
14	9.48	0	0	0	0.63	0.09	0.01	0.1	0	0	0.03	0.52	0.03	0	0.01

Table A4. Extension: instrument is ethnicity.

Source	SS	df	MS	Number of obs	=	359
				F(14, 344)	=	8.56
Model	23.0146839	14	1.643906	Prob > F	=	0
Residual	66.066096	344	0.192053	R−squared	=	0.2584
				Adj R−squared	=	0.2282
Total	89.0807799	358	0.248829	Root MSE	=	0.43824
	Coefficient	Std. err.	t	P > t	[95% conf. interval]
Gender	−0.102277	0.063947	−1.6	0.111	−0.2280524	0.023498
Dependency ratio	0.0152228	0.037261	0.41	0.683	−0.0580655	0.088511
Out-migration	−0.0914561	0.048255	−1.9	0.059	−0.1863681	0.003456
Education	0.0173144	0.007397	2.34	0.02	0.0027647	0.031864
Farming years	0.0050328	0.002198	2.29	0.023	0.0007105	0.009355
Income	0.0003402	0.000158	2.15	0.032	0.0000296	0.000651
Membership	0.2791628	0.065422	4.27	0	0.1504861	0.407839
Rice plots	0.008055	0.032028	0.25	0.802	−0.0549405	0.071051
Rice sold	0.0739542	0.06612	1.12	0.264	−0.0560958	0.204004
Rice area	−0.1321851	0.090535	−1.46	0.145	−0.3102576	0.045887
Climate	0.0215655	0.068764	0.31	0.754	−0.1136852	0.156816
Ethnicity	0.0897398	0.051273	1.75	0.081	−0.0111072	0.190587
Risk averse	0.2795078	0.151108	1.85	0.065	−0.0177038	0.576719
Literate members	0.0014804	0.017701	0.08	0.933	−0.0333354	0.036296
Constant	−0.2983857	0.182842	−1.63	0.104	−0.6580147	0.061243

Table A5. Training: Instrument is risk-averse producers.

Source	SS	df	MS	Number of obs	=	3 59
				F(14, 344)	=	8 .03
Model	21.785618	14	1.556116	Prob > F	=	0
Residual	66.6767776	344	0.193828	R−squared	=	0 .2463
				Adj R−squared	=	0 .2156
Total	88.4623955	358	0.247102	Root MSE	=	0 .44026
	Coefficient	Std. err.	t	P > t	[95% conf. interval]
Gender	−0.0701564	0.064241	−1.09	0.276	−0.19651	0.056199
Dependency ratio	−0.0245381	0.037433	−0.66	0.513	−0.09816	0.049088
Out-migration	−0.0350346	0.048478	−0.72	0.47	−0.13038	0.060315
Education	0.0113768	0.007432	1.53	0.127	−0.00324	0.025994
Farming years	0.0077157	0.002208	3.49	0.001	0.003374	0.012058
Income	0.0001923	0.000159	1.21	0.226	−0.00012	0.000504
Membership	0.3006126	0.065723	4.57	0	0.171343	0.429883
Rice plots	0.0144904	0.032176	0.45	0.653	−0.0488	0.077776
Rice sold	0.0490551	0.066425	0.74	0.461	−0.08159	0.179705
Rice area	−0.2217237	0.090953	−2.44	0.015	−0.40062	−0.04283
Climate	0.1083801	0.069081	1.57	0.118	−0.02749	0.244254
Ethnicity	0.0047764	0.051509	0.09	0.926	−0.09654	0.106088
Risk averse	0.2536526	0.151805	1.67	0.096	−0.04493	0.552235
Literate members	0.0085106	0.017783	0.48	0.633	−0.02647	0.043487
Constant	−0.3819974	0.183685	−2.08	0.038	−0.74328	−0.02071

Table A6. Information: instrument is the literacy rate in the family.

Source	SS	df	MS	Number of obs	=	3 59
				F(14, 344)	=	2 4.34
Model	44.2866983	14	3.163336	Prob > F	=	0
Residual	44.7049451	344	0.129956	R-squared	=	0 .4977
				Adj R-squared	=	0 .4772
Total	88.9916435	358	0.24858	Root MSE	=	0 .36049
	Coefficient	Std. err.	t	P > t	[95% conf. interval]
Gender	−0.0840612	0.052602	−1.6	0.111	−0.18752	0.019402
Dependency ratio	−0.053047	0.030651	−1.73	0.084	−0.11333	0.00724
Out-migration	−0.0828835	0.039695	−2.09	0.038	−0.16096	−0.00481
Education	0.0174543	0.006085	2.87	0.004	0.005486	0.029423
Farming years	−0.0002814	0.001808	−0.16	0.876	−0.00384	0.003274
Income	0.0002853	0.00013	2.2	0.029	2.98E−05	0.000541
Membership	0.3471369	0.053816	6.45	0	0.241288	0.452986
Rice plots	0.021845	0.026346	0.83	0.408	−0.02998	0.073665
Rice sold	0.0789426	0.05439	1.45	0.148	−0.02804	0.185922
Rice area	−0.1338541	0.074474	−1.8	0.073	−0.28034	0.012628
Climate	0.195289	0.056565	3.45	0.001	0.084032	0.306546
Ethnicity	0.0887778	0.042177	2.1	0.036	0.005821	0.171735
Risk averse	0.0170403	0.124301	0.14	0.891	−0.22745	0.261526
Literate members	0.0349844	0.014561	2.4	0.017	0.006345	0.063624
Constant	−0.1916093	0.150406	−1.27	0.204	−0.48744	0.104221

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