Technical efficiency of rice seed production and their determinants in Chitwan, Nepal – A one-step scaling stochastic frontier approach

Figures & data

Figure 1. Conceptual framework of the study.

Figure 2. Map of the study area. Source: ArcGIS.

Table 1. List of variables for the efficiency measurement.

Variables	Description	Expected sign
Output
Ln (Output)	Total production of rice seed (kg) per ha
Inputs
Ln (Labor)	Total man-days (days)	±
Ln (Urea)	Total amount of urea used (kg/ha)	+
Ln (Seed)	Total amount of seeds required (kg/ha)	+
Ln (Herbicide)	Total herbicide used (liters/ha)	±
Ln (Land)	Total land under cultivation (ha)	+

Note: Ln refers to the natural logarithm of the respective variables.

Table 2. List of exogenous variables for the inefficiency effect model.

Variables	Description
Family members in rice seed production	Number of family members involved in rice seed production
Education level of household head (HHH)	Number of years of formal schooling
Experience of HHH	Number of years of involvement in rice seed production
Contact with extension agent	1 denotes farm households having contact to agricultural extension agent and 0 = otherwise
Cooperative involvement	1 if member of cooperative and 0 = otherwise
Training related to rice seed production	1 if farmer received training related to rice seed production and 0 = otherwise
Adaptation to climate change	1 denotes farm household adopts a climate change adaptation strategy (Appendix D) and 0 = otherwise
Crop insurance	1 if farm has done crop insurance and 0 = otherwise
Received subsidy	1= received and 0 = did not receive

Table 3. Interpretation of the level of productivity (return to scale).

Return to scale (RTS)	Explanation
<1	Decreasing return to scale
=1	Constant return to scale
>1	Increasing return to scale

Figure 3. Socio-demographic characteristics of the respondent in Chitwan.

Figure 4. Socio-economic characteristics of the respondent in Chitwan.

Table 4. Labor, machinery and market availability in the study area.

Variables	Bharatpur	Madi
Labor availability (% respondents)	87.18	74.63
Machineries availability (% respondents)	94.87	64.2
Local market access (% respondents)	8.33	14.93
National market access (% respondents)	91.67	85.07

Table 5. Association among the socio-economic variables with the local levels in Chitwan.

Variable	Overall	Bharatpur	Madi	χ^2 value	p Value
Gender
Male	183 (82.06)	129 (57.85)	54 (24.22)	0.139	.708
Female	40 (17.94)	27 (12.11)	13 (5.83)
Occupation
Agriculture	174 (78.03)	116 (52.02)	58 (26.01)	4.07**	.044
Other	49 (21.97)	40 (17.94)	9 (4.04)
Total	223 (100)	156 (69.96)	67 (30.04)

Notes: ** represents significance at the 5% level. The figures in parentheses indicate relative percentages.

Table 6. Association among the institutional involvement with the local levels of Chitwan.

Variables	Overall	Bharatpur	Madi	χ^2 value	p Value
Cooperative involvement
Yes	105(47.09)	77(34.53)	28 (12.56)	1.077	.299
No	118(52.91)	79(35.43)	39 (17.49)
Training
Received	99 (44.39)	72 (32.29)	27 (12.11)	0.65	.42
Not received	124(55.61)	84 (37.67)	40 (17.94)
Contact extension agent
Contact	148 (66.37)	116(52.02)	32 (14.35)	14.85***	.00
No contact	75 (33.63)	40 (17.94)	35 (15.70)
Total	223 (100)	156 (69.96)	67 (30.04)

Notes: *** represents significance at the 1% level. The figures in parentheses represent relative percentages.

Table 7. Association of the adoption of crop insurance, climate change adaptation strategies and receipt of subsidy with the local levels of Chitwan.

Variable	Overall	Bharatpur	Madi	χ^2 value	p Value
Crop insurance
Adopted	65 (29.15)	34 (15.25)	31 (13.9)	13.59***	.001
Not adopted	158 (70.85)	122 (54.71)	36 (16.14)
Subsidy
Received	147 (65.92)	118 (53)	29 (13)	21.84***	.001
Did not receive	76 (34.08)	38 (17)	38 (17)
Adaptation strategies
Adopted	149 (66.82)	114 (51.12)	35 (15.70)	9.18***	.00
Not-adopted	74 (33.18)	42 (18.83)	32 (14.35)
Total	223 (100)	156 (69.96)	67 (30.04)

Notes: *** represents significance at the 1% level. The figures in parentheses represent relative percentages.

Figure 5. Genotype of rice seed grown by the rice seed growers in Chitwan.

Table 8. Maximum likelihood estimates for Bharatpur from the truncated normal scaling property model.

Log likelihood = 83.13 LR test (χ^2 = 49.99***, p < .001)			Number of observations = 156
			Wald χ^2 (5) = 2595.82
			Prob > χ^2 = 0.001
Ln (prod)	Coef	Std. err	z	p>|z|	[95% conf. interval]
Frontier
Ln_Land	0.067	0.026	2.60***	.009	0.017	−0.12
Ln_Labor	0.629	0.070	8.87***	.001	0.49	0.77
Ln_Urea	0.140	0.045	3.11***	.002	0.05	0.23
Ln_Herbicide	0.320	0.068	4.78***	.001	0.19	0.46
Ln_Seed	0.012	0.032	0.37	.712	−0.05	0.08
_const	0.390	0.380	1.01	.311	−0.36	1.13
Hscale
Member_riceseed	−0.795	0.423	−1.88*	.060	−1.62	−0.033
Subsidy_seed	0.457	0.485	0.94	.345	−0.49	1.40
Crop insurance	0.767	1.085	0.71	.480	−1.36	2.89
Adop_Climate	−2.56	1.484	−1.72*	.085	−5.46	0.35
Education	0.123	0.065	1.89*	.059	−0.004	0.25
Experience	0.149	0.071	2.09**	.037	0.009	0.28
Cooperative	0.045	0.349	0.13	.897	−0.639	0.73
Extworker_visit	−2.18	0.813	−2.68***	.007	−3.77	−0.58
Training	−1.05	0.621	−1.69*	.090	−2.26	0.16
Tau
_cons	0.089	0.098	0.91	.362	−0.103	0.28
Cu
_cons	−6.22	2.41	−2.58**	.010	−10.96	−1.49
Vsigmas
_cons	−4.014	0.121	−33.12***	.001	−4.25	−3.77
Variable	Coef.	Std. err	t	p>|t|	[95% conf. interval]
Sigma_v_sqr	0.018	0.002	8.25***	.000	0.014	0.023

Notes: *, ** and *** represent significant at 10%, 5% and 1% levels, respectively. The transformation cannot be done for sigma_u_sqr in the scaling-property model since it is a function of variables. The transformation is done only if the sigma_u_sqr is constant.

Table 9. Maximum likelihood estimates for Madi from the truncated normal scaling property model.

Log likelihood = 125.47 LR test (χ^2 = 62.21***, p < .001)		Number of observations = 67 Wald χ^2 (5) = 1075.25 Prob > χ^2 = 0.001
lnp	Coef	Std. err	z	p>|z|	[95% conf. interval]
Frontier
Ln_Land	0.03	0.03	1.03	.302	−0.03	0.09
Ln_Labor	0.69	0.05	15.15***	.001	0.60	0.78
Ln_Urea	0.14	0.03	4.80***	.001	0.08	0.19
Ln_Herbicide	0.06	0.02	2.52**	.012	0.01	0.10
Ln_Seed	0.02	0.03	0.85	.401	−0.03	0.08
_cons	−0.94	0.43	−2.17	.031	−1.79	−0.09
hscale
Experience	−0.06	0.02	−2.69***	.007	−0.10	−0.02
Extworker_visit	−0.19	0.09	−2.12**	.034	−0.37	−0.01
Adopt_Climate	−0.42	0.25	−1.66*	.096	−0.92	0.08
Crop insurance	0.22	0.18	1.21	.225	−0.14	0.58
Training	−0.16	0.15	−1.07	.284	−0.44	0.13
Subsidy_seed	0.14	0.15	0.95	.341	−0.15	0.44
Tau
_cons	0.44	0.09	4.98***	.001	0.27	0.61
Cu
_cons	−8.54	9.05	−0.94	.352	−26.27	9.19
Vsigmas
_cons	−6.63	0.45	−14.7***	.001	−7.51	−5.75
Variable	Coef.	Std. err.	t	p > t	[95% conf. interval]
Sigma_v_sqr	0.0013	0.00059	2.22**	.026	0.000	0.0032

Notes: *, ** and *** represent significant at 10%, 5% and 1% levels, respectively. The transformation cannot be done for sigma_u_sqr in the scaling-property model since it is a function of variables. The transformation is done only if the sigma_u_sqr is constant.

Table 10. Technical efficiency (TE) scores of rice seed production in Chitwan.

	Location
Efficiency scores	Bharatpur		Madi
	Frequency	Percentage	Frequency	Percentage
<50%	0	0	0	0
51–60%	4	2.56	0	0
61–70%	0	0	3	4.48
71–80%	6	3.85	34	50.75
81–90%	11	7.05	23	34.33
91–100%	135	86.54	7	10.45
Total	156	100	67	100
Mean TE (%)	96.15		79.61
Min TE (%)	55.99		67.55
Max TE (%)	99.99		92.35
Std. dev	0.07		0.072

Table 11. Independent sample t-test for comparison of the mean technical efficiency between the adopters and non-adopters of climate change adaptation strategy in Chitwan.

Groups	Obs	Mean	Std. err	Std. dev	95% C.I		t-value	p Value
Adopters	149	0.953	0.010	0.080	0.931	0.975
Non-adopters	74	0.909	0.009	0.118	0.891	0.928
Combined	223	0.921	0.007	0.111	0.906	0.935
Difference		0.043	0.014		0.015	0.072	3.054***	.002

Note: *** represent significant at 1% level.

Table 12. Return to scale for rice seed production in Bharatpur Metropolitan City and Madi Municipality.

	Elasticity
Input variables	Bharatpur	Madi
Land	0.067	0.03
Labor	0.629	0.69
Urea	0.14	0.14
Seed	0.012	0.02
Herbicide	0.32	0.06
Return to scale	1.17	0.94

Figure 6. Rank of important factors for rice seed production.

Figure 7. Rank of major problems in rice seed production.

Table A1. Skewness test for confirmation of the presence of inefficiency.

Skewness/kurtosis tests for normality						Decision for inefficiency
Variable	n	Pr (skewness)	Pr (kurtosis)	------ Joint ------
				Χ^2 (2)	Prob > chi^2
Efficiency (Bharatpur)	156	0.0005	0.0217	17.38***	0.0002	Presence
Efficiency (Madi)	67	0.0449	0.0920	6.86**	0.0324	Presence

Note: ‘**’ and ‘***’ represent significance at 5% and 1% levels, respectively.

Table B1. Critical values for the mixed chi-square distribution.

Significance Level
dof	0.25	0.1	0.05	0.025	0.01	0.005	0.001
2	2.090	3.808	5.138	6.483	8.273	9.634	12.810

Source: Kodde and Palm (1986).

Figure C1. Distribution of technical efficiency for Bharatpur and Madi, respectively.

Table D1. Prioritization of the major climate change adaptation strategies in the study area.

Options Criteria	Varietal selection	Residue management	Water management	Planting time	Nutrient management
Effectiveness	5	5	4	5	4
External effects	5	4	5	4	3
Availability	4	5	3	4	4
Accessibility	5	4	4	2	4
Affordability	4	3	3	5	3
Profitability	5	5	5	5	5
Total mark	28	26	24	25	23
Rank	I	II	IV	III	V

Notes: Where, the scale of 0–5 represents; 0 = no significance 1= low significance 2 = low significance 3 = moderate significance 4 = high significance and 5 = strong significance.

Kodde, D. A., & Palm, F. C. (1986). Wald criteria for jointly testing equality and inequality restrictions. Econometrica, 54(5), 1243. https://doi.org/10.2307/1912331

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Data availability statement

The data that support the findings of this study are available from the corresponding author, Rabin Thapa, upon reasonable request.