Downloads provided by UsageCounts
In the present study optimal solutions were found for net farm returns using Linear Programming model on the sample farmers of Bidar District.The LINGO 17.0 package was used to get the solutions. The sample was of 120 small and large farmers collected from 15 villages from five Tehsils. From each village eight farmers comprising of small and large farmers were selected. A total of EIGHT models were developed. They were classified as small farmers S1, S2, S3, S4 and large farmers L1, L2, L3, and L4. The results were compared with existing cropping pattern of small and large farmers. The model S1, small farmers with existing technology and restricted capital registered an increase of net returns per hectare by 27%, S2 small farmer with existing technology and relaxed capital returns increased by 34%, S3 small farmer with recommended technology and restricted capital, returns increased by 55%, S4 small farmer with recommended technology and relaxed capital, the returns increased by 65% per hectare. Similarly the net returns per hectare in case of large farmers L1, L2, L3, L4 increased by 47%, 65%, 49%, 76% respectively. The impact of credit on net farm returns in small farmers was Rs: 8322 and the same in large farmers was Rs: 615276. It was noted that credit played an important role in augmenting income of farmers; the credit required was directly related to farm size while credit on income, inversely related to farm size.
restricted capital cropping pattern reorganization of resources impact of credit Linear Programming Model net farmers returns.
restricted capital cropping pattern reorganization of resources impact of credit Linear Programming Model net farmers returns.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
| views | 4 | |
| downloads | 6 |

Views provided by UsageCounts
Downloads provided by UsageCounts