Water Users Association and Irrigation Management - Institute for ...
Water Users Association and Irrigation Management - Institute for ...
Water Users Association and Irrigation Management - Institute for ...
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have merged the in<strong>for</strong>mation from both the villages <strong>and</strong> then estimated the logit regression.<br />
Farmers who attached more importance to the problems of soil degradation assume the<br />
value I <strong>and</strong> 0 otherwise.<br />
The results suggest that in Hagedal, the migrant farmers from Andhra Pradesh with timely<br />
availability of credit <strong>and</strong> better non-farm income are more likely to adopt timely<br />
management strategies. Andhra Pradesh farmers were traditionally paddy growers. Because<br />
of their long experience in paddy cultivation they are aware of the impacts of<br />
monocropping on soil <strong>and</strong> hence they are more likely to adopt the management strategies<br />
than the native farmers who had little experience. Farmers who got timely credit could<br />
access fertilizers <strong>and</strong> soil amendments like gypsum <strong>and</strong> zinc unlike the farmers who could<br />
not get timely credit. Hence, they are more likely to adopt management strategies. The<br />
Kissan Credit Card Scheme introduced by the government in 1998-99, as an innovative<br />
scheme to facilitate easy credit to the farmers, has not yet gained popularity in the village.<br />
Farmers, there<strong>for</strong>e, mainly depend on private moneylenders. Farmers with an increase in<br />
non-farm income are more likely to divert the income into management strategies so as to<br />
enhance their production. The other variables, except TFM, have the expected signs<br />
although they turned out to be statistically not significant.<br />
Table 6.5: LOl!;it Estimates of the Likeh 'h ood 0 fAd ~tlOn 0 fM anagemen t St ra t egles .<br />
Variable<br />
I<br />
Gundur<br />
Hagedal<br />
Coefficient Z-statistic Coefficient Z-statistic<br />
Cattle (number) 0.946*** 1.885 0.209<br />
1.322<br />
Factor use (dummy) 2.529*** I. 719 0.442<br />
0.679<br />
Experience (years) 0.19*** 1.717 0.0004 0.018<br />
Credit (dummy) 0.103 0.071 0.983*** 1.652<br />
Non-farm/farm 0.442 0.435 1.662*** 1.693<br />
Education (years) -0.311 -1.466 0.044<br />
0.667<br />
TFM (numbers) -0.379 -I. 505 -0.032 -0.496<br />
Mother tongue (dummy) -2.318 -1.545 -1.094* -2.489<br />
Chi-square 44.903*<br />
28.316*<br />
Predictability 87.2%<br />
73.9%<br />
WUA 0.961** Z-statistic 2.94<br />
• SIgnificance al 1% .<br />
•• Significance at 5% .<br />
••• Signitlcance at IO'Yo. .<br />
Factor use ~ Capacity to use faclors like labor, tractor. etc. I tor yes <strong>and</strong> 0 <strong>for</strong> no.<br />
Credit ~ Timely availability of credit (I <strong>for</strong> yes. 0 <strong>for</strong> 110).<br />
Non farmlfarm ~ Non farm income as a ratio of farm income.<br />
TFM ~ Number of members in the family.<br />
Mother Tongue ~ I <strong>for</strong> Telugu, 2 <strong>for</strong> Kannada, <strong>and</strong> 3 <strong>for</strong> Urdu.<br />
144