Assessing different systems for enhancing farm income and resilience in extreme dry region of India
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Kumar Shalander, Amare Haileslassie, Thiagarajah Ramilan, Suhas Wani. (7/2/2014). Assessing different systems for enhancing farm income and resilience in extreme dry region of India. New South Wales, Australia.
Enhancing resilience among small farm households in dry regions is important to cope with extreme weather conditions and changing climate. This study presents the results of assessment made in Western Rajasthan (Thar) covering 19 million ha area which is the most densely populated desert region in the world. Agriculture which is the important source of livelihood for large number of poor small farm holders in India is very risk prone although, agriculture's contribution to the GDP is decreasing over time. Besides livestock, farmers traditionally diversify their activity mix with forestry and horticulture. It has been well proved by many studies that the introduction of suitable perennial component in arid farming systems not only enhances farm income and family nutrition but also its resilience under water scarcity. But it has not been promoted and adopted in systems perspective as a commercially viable model. Using this case study we analyze the potential economic benefits and suitability of such models under different resource situations. The examined models are integrated horticulture and agro-forestry with suitable species together with rainwater harvesting structures that significantly enhances farm income and resilience. The annual net returns on different farm types in arid region would increase in multiple folds. The results suggest the need for reorientation of the agricultural research for development agenda taking into consideration the existing and emerging abiotic stresses, and the development and dissemination of new impact pathway through integration and convergence to intertwine the technologies with enabling institutions, policies and financial instruments as a win-win proposition through business model.