Barriers to the adoption of multiple agricultural innovations: insights from Bt cotton, wheat seeds, herbicides and no-tillage in Pakistan

Figures & data

Figure 1. Map of Pakistan pointing to the study area; source: own elaboration. (This map is developed using shapefiles from https://geodata.lib.utexas.edu and ggplot2 package in R statistical software; Wickham, 2016.)

Table 1. Salient indicators of the survey area.

Indicators	Bahawalpur Division	Multan Division	Sahiwal Division	Entire Punjab Province	% Share of survey area from Punjab
Total reported area (1000 ha)*	2142	1524	1032	17,518	27
Area sowed under cotton (1000 ha)*	664	712	102	2053	72
Area sowed under wheat (1000 ha)*	930	835	479	6560	34
Total tube well (numbers)*	114,412	63,801	75,243	1,100,841	23
Total tractors (numbers)*	51,554	43,027	32,729	331,905	38
Total threshers (numbers)*	23,449	20,211	14,202	145,155	40
Harvesters/reapers (numbers)*	10,434	4179	2230	40,285	42
Population (1000 persons)*	11,464	12,265	7380	110,012	28
Rural population (1000 persons)**	8630	8851	5731	69,442	33
Literacy of rural areas (ratio)*	0.26	0.30	0.39	0.38
Literacy of urban areas (ratio)*	0.57	0.61	0.66	0.65

Data source:*Government of Punjab (2019) and **Government of Pakistan (2017).

Table 2. Descriptive statistics.

	Variable	Mean	Std. Dev.	Min	Max
Dependant variables	No-tillage (yes = 1; no = 0)	0.46	0.50	0.00	1.00
	Herbicides of cutting-edge technology (yes = 1; no = 0)	0.52	0.50	0.00	1.00
	Bt cotton seed (yes = 1; no = 0)	0.65	0.48	0.00	1.00
	High-quality wheat seeds (yes = 1; no = 0)	0.61	0.49	0.00	1.00
Explanatory variables	Age of the respondents (years)	43.53	12.92	17.00	73.00
	Household size (numbers)	7.62	3.40	2.00	20.00
	No of male (numbers)	3.40	1.98	1.00	13.00
	Male above18 years	2.43	1.57	0.00	10.00
	Education in years	5.71	4.41	0.00	16.00
	Area sowed under cotton (ha)	0.76	0.58	0.00	2.02
	Area sowed under wheat (ha)	0.93	0.52	0.00	2.02
	Off farm income (yes = 1; no = 0)	0.46	0.50	0.00	1.00
	No of visits by agri-extension (numbers/month)	0.80	0.95	0.00	4.00
	Access to credit (yes = 1; no = 0)	0.41	0.49	0.00	1.00
	Laser level (yes = 1; no = 0)	0.79	0.41	0.00	1.00
	Ownership of farm machinery (yes = 1; no = 0)	0.23	0.42	0.00	1.00
	Farm machinery index^a(weighted by present value)	0.14	0.30	0.00	1.00
	No of milch animals (numbers)	1.26	1.60	0.00	10.00
	Farm distance to output market (km)	13.19	8.83	0.00	100.00
	Farm distance to input market (km)	11.52	7.88	0.00	60.00
	Farm distance to the main road (km)	1.11	1.08	0.00	6.00
	Membership in a farmers’ union (yes = 1; no = 0)	0.11	0.31	0.00	1.00
	Agri-extension-info via radio (yes = 1; no = 0)	0.26	0.44	0.00	1.00
	Experts hired for spray (yes = 1; no = 0)	0.33	0.47	0.00	1.00

Source: Field survey results.

^a Total present value in Pakistani rupees of agricultural fixed assets owned by the small farming household, including any tractors, threshers, rotavators, trolleys, ploughs and spraying machines weighted by the average percentage share.

Figure 2. Confidence interval plots of demographic and structural factor of the farm management of smallholder farming households concerning adoption. Source: Field survey results.

Table 3. Smallholder farming households’ distribution with respect to adoption decisions.

Adoption decisions	Adoption status	Frequency	Percent	Cumulative
Exclusive non-adoption	0	43	15.64	15.64
Single input adoption	1	45	16.36	32.00
Two inputs’ adoption	2	60	21.82	53.82
Three inputs’ adoption	3	57	20.73	74.55
Exclusive adoption of all inputs	4	70	25.45	100.00
Total		275	100.00

Source: Field survey results.

Table 4. Estimates of the multivariate probit models variable.

Variable	Coefficient	Marginal Effects	Robust SE
No-tillage (yes = 1; no = 0)
Education in years	0.020	0.01	0.020
No of agri-extension visits (numbers)	0.141	0.05	0.090
Access to credit (yes = 1; no = 0)	−0.061	−0.02	0.172
Laser level (yes = 1; no = 0)	0.797***	0.27	0.215
Farm machinery index	0.353	0.12	0.284
Off-farm income (yes = 1; no = 0)	−0.247	−0.08	0.167
Area sowed under wheat (ha)	−0.580***	−0.20	0.197
Area sowed under cotton (ha)	0.904***	0.31	0.166
No of milch animals	0.043	0.01	0.054
Bt cotton seed (yes = 1; no = 0)
Education in years	0.063**	0.02	0.023
No of agri-extension visits (numbers)	0.160*	0.05	0.097
Access to credit (yes = 1; no = 0)	0.038	0.01	0.191
Farm distance to input market (km)	−0.029**	−0.01	0.015
Farm distance to output market (km)	0.001	0.00	0.009
Farm distance to the main road (km)	0.006	0.00	0.094
Laser level (yes = 1; no = 0)	0.456*	0.15	0.250
Farm machinery index	0.404	0.14	0.331
Off farm income (yes = 1; no = 0)	0.396**	0.13	0.189
Area sowed under wheat (ha)	−0.870***	−0.29	0.257
Area sowed under cotton (ha)	1.806***	0.61	0.262
Membership in a farmers’ union (yes = 1; no = 0)	−0.085	−0.03	0.337
No of milch animals	0.010	0.00	0.057
High-quality wheat seed (yes = 1; no = 0)
Education in years	0.033	0.01	0.021
No of agri-extension visits (numbers)	0.131	0.04	0.093
Access to credit (yes = 1; no = 0)	0.010	0.00	0.171
Farm distance to input market (km)	−0.003	−0.00	0.012
Farm distance to output market (km)	−0.020*	−0.01	0.011
Farm distance to the main road (km)	−0.063	−0.02	0.078
Laser level (yes = 1; no = 0)	0.300	0.10	0.214
Farm machinery index	0.516*	0.17	0.319
Off farm income (yes = 1; no = 0)	0.460***	0.16	0.174
Area sowed under wheat (ha)	0.257	0.09	0.213
Area sowed under cotton (ha)	0.560***	0.19	0.179
Membership in a farmers’ union (yes = 1; no = 0)	−0.130	−0.04	0.294
No of milch animals	0.112**	0.04	0.055
Herbicides of cutting-edge technology (yes = 1; no = 0)
Education in years	0.068***	0.02	0.020
No of agri-extension visits (numbers)	0.305***	0.10	0.097
Access to credit (yes = 1; no = 0)	−0.152	−0.05	0.179
Farm distance to input market (km)	−0.005	−0.00	0.013
Farm distance to output market (km)	−0.002	−0.00	0.010
Farm distance to the main road (km)	−0.092	−0.03	0.078
Laser level (yes = 1; no = 0)	0.376*	0.13	0.210
Farm machinery index	0.539*	0.18	0.309
Off farm income (yes = 1; no = 0)	0.346**	0.12	0.178
Area sowed under wheat (ha)	−0.346*	−0.12	0.205
Area sowed under cotton (ha)	0.644***	0.22	0.185
Membership in a farmers’ union (yes = 1; no = 0)	−0.066	−0.02	0.283
No of milch animals	−0.015	−0.01	0.053
$\rho$TB	0.239***		0.119
$\rho$TW	0.269***		0.105
$\rho$TH	0.379***		0.098
$\rho$BW	0.841***		0.048
$\rho$BH	0.231***		0.114
$\rho$WH	0.305***		0.099
Log pseudo-likelihood	−546.252
Wald ${\chi }^2$(48)	211.80***
Likelihood ratio test of $\rho$21=$\rho$31=$\rho$41=$\rho$32=$\rho$42=$\rho$43 = 0: ${\chi }^2$(6)	98.892***
Number of observations	275

Notes: The level of significance is *** p < 0.01, ** p < 0.05, * p < 0.1. ρs indicate the correlation coefficients among smallholder farming households’ adopted technologies/farming practices; software STATA 17; number of random draws (R = 150).

Table A1. Estimates of the probit models.

No-tillage	Coefficient	Robust SE
Education in years	0.020	0.020
No of agri-extension visits (numbers)	0.148	0.090
Access to credit (yes = 1; no = 0)	−0.056	0.174
Laser level (yes = 1; no = 0)	0.829***	0.222
Farm machinery index	0.347	0.289
Off-farm income (yes = 1; no = 0)	−0.233	0.168
Area sowed under wheat (ha)	−0.613***	0.205
Area sowed under cotton (ha)	0.928***	0.173
No of milch animals	0.042	0.053
Number of observations	275	Bayesian crit. (BIC)	382.383
Prob > ${\chi }^2$(9)	49.438***	Akaike crit. (AIC)	346.215
Link test hatsq p-value	0.417	Hosmer–Lemeshow test ${\chi }^2$(8) = 9.68	Prob > ${\chi }^2$ = 0.2885

Notes: *** p < .01, ** p < .05, * p < .1.

Table A2. Estimates of the probit models.

Bt cotton seed	Coefficient	Robust SE
Education in years	0.065***	0.024
No of agri-extension visits (numbers)	0.164*	0.098
Access to credit (yes = 1; no = 0)	0.074	0.196
Farm distance to input market (km)	−0.024	0.015
Farm distance to output market (km)	−0.003	0.011
Farm distance to the main road (km)	−0.004	0.103
Laser level (yes = 1; no = 0)	0.481**	0.249
Farm machinery index	0.502	0.362
Off farm income (yes = 1; no = 0)	0.410**	0.194
Area sowed under wheat (ha)	−1.250***	0.292
Area sowed under cotton (ha)	2.018***	0.280
Membership in a farmers’ union (yes = 1; no = 0)	−0.090	0.346
No of milch animals	0.0350	0.061
Number of observations	275	Bayesian crit. (BIC)	315.777
Prob > ${\chi }^2$(13)	80.265***	Akaike crit. (AIC)	265.142
Hosmer–Lemeshow test ${\chi }^2$(8) = 10.56		Prob > ${\chi }^2$ = 0.22

*** p < .01, ** p < .05, * p < .1.

Table A3. Estimates of the probit models.

High-quality wheat seed	Coefficient	Robust SE
Education in years	0.031	0.021
No of agri-extension visits (numbers)	0.113	0.099
Access to credit (yes = 1; no = 0)	0.011	0.176
Farm distance to input market (km)	0.001	0.013
Farm distance to output market (km)	−0.019*	0.011
Farm distance to the main road (km)	−0.086	0.083
Laser level (yes = 1; no = 0)	0.314	0.216
Farm machinery index	0.567*	0.327
Off farm income (yes = 1; no = 0)	0.467***	0.177
Area sowed under wheat (ha)	0.252	0.210
Area sowed under cotton (ha)	0.558***	0.187
Membership in a farmers’ union (yes = 1; no = 0)	−0.122	0.301
No of milch animals	0.109**	0.055
Number of observations	275	Bayesian crit. (BIC)	393.182
Prob > ${\chi }^2$(13)	56.371***	Akaike crit. (AIC)	342.547
Link test hatsq p-value	0.954	Hosmer–Lemeshow test ${\chi }^2$(8) = 8.21	Prob > ${\chi }^2$ = 0.41

*** p < .01, ** p < .05, * p < .1.

Table A4. Estimates of the probit models.

Herbicides of cutting-edge technology	Coefficient	Robust SE
Education in years	0.070***	0.021
No of agri-extension visits (numbers)	0.307***	0.099
Access to credit (yes = 1; no = 0)	−0.144	0.181
Farm distance to input market (km)	0.001	0.013
Farm distance to output market (km)	−0.003	0.011
Farm distance to the main road (km)	−0.125	0.082
Laser level (yes = 1; no = 0)	0.400*	0.218
Farm machinery index	0.578*	0.321
Off farm income (yes = 1; no = 0)	0.352**	0.179
Area sowed under wheat (ha)	−0.372*	0.208
Area sowed under cotton (ha)	0.673***	0.190
Membership in a farmers’ union (yes = 1; no = 0)	−0.058	0.296
No of milch animals	−0.016	0.052
Number of observations	275	Bayesian crit. (BIC)	392.128
Prob > ${\chi }^2$(13)	51.683***	Akaike crit. (AIC)	341.493
Link test hatsq p-value	0.245	Hosmer–Lemeshow test ${\chi }^2$(8) = 5.32	Prob > ${\chi }^2$ = 0.72

Notes: *** p < .01, ** p < .05, * p < .1.

Wickham, H. (2016). Ggplot2: Elegant graphics for data analysis. Springer Nature. https://doi.org/10.1007/978-3-319-24277-4

 Google Scholar

Government of Punjab. (2019). Bureau of Statistics. Government of Punjab. https://www.bos.gop.pk/developmentstat

 Google Scholar

Government of Pakistan. (2017). Punjab Population 2017. Pakistan Bureau of Statistics (PBS). https://www.pbs.gov.pk/sites/default/files/population/2017/punjab_tehsil.pdf

 Google Scholar