3. Non-Climatic Variables: The fertilizer consumption data was taken from
Fertilizer Statistics 1990–1995, 1998–2004, 2005–2009, by The Fertilizer
Association of India. The Indian Agricultural Statistics Vol. II, 1966/67–
1987/88, by the Directorate of Economics and Statistics, Govt. of India,
Brochure on Irrigation Statistics in Karnataka 1980/81–1993/94, and Karnataka
at a Glance 1995–96, published by Directorate of Economics and Statistics,
Govt. of Karnataka gives data on the total sugarcane area irrigated.
4 Empirical Framework
Production function models, Ricardian cross sectional regression model,
Agronomic-economic model, Agro-ecological zone model and integrated assessment model (United Nations Report 2011) are some of the commonly used
econometric approaches in the existing literature to analyse the impact of climatic
variables on yield of agricultural output. In this particular study, we use panel data
regression estimation to analyse the impact of climatic and non-climatic variables
on sugarcane yield.
A Hausman’s Test (1978) specification test was used and fixed effects model of
panel data estimation was identified as the best fit model. Finally, Prais–Winsten
models with panels corrected standard errors (PCSEs) estimation are used for the
proposed regression models to avoid the problems of heteroskedasticity, serial
correlation, auto-correlation and serial auto-correlation in fixed effects regression
model (Gupta 2012; Kumar and Sharma 2014).
Table 1 A descriptive analysis of the data
Variable
Obs
Mean
Std. Dev.
Min
Max
Yield
592
8.439711
1.874944
3.25641
19.48148
Fertilizer
592
33884.51
29803.61
580
179806
Area_irrign
592
11.50725
20.42047
0
174.71
Max_winter
592
30.80219
1.798176
24.756
33.78075
Max_summer
592
32.96593
3.137419
24.947
39.64933
Max_monsoon
592
28.27522
2.100356
22.272
32.36633
Max_autumn
592
29.31305
1.754337
23.622
33.066
Min_winter
592
18.40842
.9610739
16.164
20.96225
Min_summer
592
22.64056
1.593508
18.69033
26.593
Min_monsoon
592
21.15136
1.240445
17.70833
23.33067
Min_autumn
592
19.85836
1.064107
16.798
22.3585
Rain_winter
592
5.600495
6.009835
0
40.52675
Rain_summer
592
152.5724
116.8271
12.62933
661.695
Rain_monsoon
592
244.4693
159.2002
41.517
827.376
Rain_autumn
592
98.74228
44.88098
3.9975
293.9725
394
A.B. Chandran and K.N. Anushree
Fertilizer Statistics 1990–1995, 1998–2004, 2005–2009, by The Fertilizer
Association of India. The Indian Agricultural Statistics Vol. II, 1966/67–
1987/88, by the Directorate of Economics and Statistics, Govt. of India,
Brochure on Irrigation Statistics in Karnataka 1980/81–1993/94, and Karnataka
at a Glance 1995–96, published by Directorate of Economics and Statistics,
Govt. of Karnataka gives data on the total sugarcane area irrigated.
4 Empirical Framework
Production function models, Ricardian cross sectional regression model,
Agronomic-economic model, Agro-ecological zone model and integrated assessment model (United Nations Report 2011) are some of the commonly used
econometric approaches in the existing literature to analyse the impact of climatic
variables on yield of agricultural output. In this particular study, we use panel data
regression estimation to analyse the impact of climatic and non-climatic variables
on sugarcane yield.
A Hausman’s Test (1978) specification test was used and fixed effects model of
panel data estimation was identified as the best fit model. Finally, Prais–Winsten
models with panels corrected standard errors (PCSEs) estimation are used for the
proposed regression models to avoid the problems of heteroskedasticity, serial
correlation, auto-correlation and serial auto-correlation in fixed effects regression
model (Gupta 2012; Kumar and Sharma 2014).
Table 1 A descriptive analysis of the data
Variable
Obs
Mean
Std. Dev.
Min
Max
Yield
592
8.439711
1.874944
3.25641
19.48148
Fertilizer
592
33884.51
29803.61
580
179806
Area_irrign
592
11.50725
20.42047
0
174.71
Max_winter
592
30.80219
1.798176
24.756
33.78075
Max_summer
592
32.96593
3.137419
24.947
39.64933
Max_monsoon
592
28.27522
2.100356
22.272
32.36633
Max_autumn
592
29.31305
1.754337
23.622
33.066
Min_winter
592
18.40842
.9610739
16.164
20.96225
Min_summer
592
22.64056
1.593508
18.69033
26.593
Min_monsoon
592
21.15136
1.240445
17.70833
23.33067
Min_autumn
592
19.85836
1.064107
16.798
22.3585
Rain_winter
592
5.600495
6.009835
0
40.52675
Rain_summer
592
152.5724
116.8271
12.62933
661.695
Rain_monsoon
592
244.4693
159.2002
41.517
827.376
Rain_autumn
592
98.74228
44.88098
3.9975
293.9725
394
A.B. Chandran and K.N. Anushree