In this study, the impact of access to irrigation on crop diversification and farm-level profits in the state of Telangana was examined. This study comprised two primary aspects. First, the determinants of crop diversification in Telangana were investigated using the logit model. Second, various farm production models were developed to evaluate the impact of irrigation enhancement sources on the farm profit function. The findings of this study indicate that the agricultural implements index and market access variables play a role in determining crop diversification in the studied villages, whereas surface irrigation has a negative effect on crop diversification. The Cobb–Douglas production function with different specifications was used in this study, which showed that the logarithms of the fertiliser variable and access to new irrigation facilities that improve groundwater table significantly enhance farm profits in the villages studied. The inclusion of a dummy variable representing agro-climatic conditions also revealed a significant effect on farm profits, indicating that farmers in north-zone districts achieve higher profits than those in central-zone districts, likely due to differences in soil fertility and crop diversification. Understanding the factors influencing farm profits is crucial for policymakers and extension agents in the state for informed decision-making and policy formulation.
AfridiF., MahajanK., & SangwanN. (2022). The gendered effects of droughts: Production shocks and labor response in agriculture. Labour Economics, 78, 102227. 10.1016/j.labeco.2022.102227
2.
AliM., & FlinnJ. C. (1989). Profit efficiency among Basmati rice producers in Pakistan Punjab. American Journal of Agricultural Economics, 71(2), 303–310. 10.2307/1241587
3.
AuffhammerM., & CarletonT. A. (2018). Regional crop diversity and weather shocks in India. Asian Development Review, 35(2), 113–130. 10.1162/adev_a_00116
4.
BatteseG. E., & CoelliT. J. (1995). A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empirical Economics, 20(2), 325–332. 10.1007/BF01205442
5.
BinswangerH. P., KhandkerS. R., & RosenzweigM. R. (1993). How infrastructure and financial institutions affect agricultural output and investment in India. Journal of Development Economics, 41(2), 337–366. 10.1016/0304-3878(93)90062-R
6.
BirthalP. S., KhanT. M., NegiD. S., & AgarwalS. (2014). Impact of climate change on yields of major food crops in India: Implications for food security. Agricultural Economics Research Review, 27(2), 145–155. 10.22004/ag.econ.196659
7.
BirthalP. S., RoyD., & NegiD. S. (2015). Assessing the impact of crop diversification on farm poverty in India. World Development, 72, 70–92. 10.1016/j.worlddev.2015.02.015
8.
CoelliT. J. (1996). A guide to FRONTIER version 4.1: A computer program for stochastic frontier production and cost function estimation (CEPA working paper no. 7). School of Economics, University of New England.
9.
DubeL., NumbwaR., & GuveyaE. (2016). Determinants of crop diversification amongst agricultural co-operators in Dundwa agricultural camp, Choma district, Zambia. Asian Journal of Agriculture and Rural Development, 6(1), 1–13. 10.22004/ag.econ.338760
10.
GulatiA., & JunejaR. (2022). Transforming Indian agriculture. In ChandR., JoshiP., & KhadkaS. (Eds.), Indian agriculture towards 2030: Pathways for enhancing farmers’ income, nutritional security and sustainable food and farm systems (pp. 9–37). Springer Nature Singapore. 10.1007/978-981-19-0763-0_2
International Food Policy Research Institute (IFPRI). (2005, June26–29). The future of small farms: Proceedings of a research workshop. IFPRI. https://pdf.usaid.gov/pdf_docs/Pnado609.pdf
13.
KasemS., & ThapaG. B. (2011). Crop diversification in Thailand: Status, determinants, and effects on income and use of inputs. Land Use Policy, 28(3), 618–628. 10.1016/j.landusepol.2010.12.001
14.
KurukulasuriyaP., & MendelsohnR. O. (2007). Crop selection: Adapting to climate change in Africa (World Bank policy research working paper no. 4307). World Bank. 10.1596/1813-9450-4307
15.
LiC. (2023). Climate change impacts on rice production in Japan: A Cobb–Douglas and panel data analysis. Ecological Indicators, 147, 110008. 10.1016/j.ecolind.2023.110008
16.
ManjunathaA. V., AnikA. R., SpeelmanS., & NuppenauE. A. (2013). Impact of land fragmentation, farm size, land ownership and crop diversity on profit and efficiency of irrigated farms in India. Land Use Policy, 31, 397–405. 10.1016/j.landusepol.2012.08.005
17.
NelA. A., & LoubserH. L. (2004). The impact of crop rotation on profitability and production risk in the Eastern and Northwestern Free State. Agrekon, 43(1), 101–111. 10.1080/03031853.2004.9523638
18.
ParikhK. S., & NagarajanH. K. (2004). How important is land consolidation? Land fragmentation and implications for productivity: Case study of village Nelpathur in Tamil Nadu (NABARD Occasional paper no. 31). National Bank for Agriculture and Rural Development. https://www.nabard.org/demo/auth/writereaddata/File/OC%2031.pdf
19.
PeddiD., & KavikumarK. S. (2021). Adoption of soil conservation measures: Evidence from rain-fed watershed areas of Telangana. Ecology, Economy and Society: The INSEE Journal, 4(2), 95–117. 10.37773/ees.v4i2.273
20.
PingaliP., MittraB., & RahmanA. (2017). The bumpy road from food to nutrition security: Slow evolution of India’s food policy. Global Food Security, 15, 77–84. 10.1016/j.gfs.2017.05.002
21.
QaimM. (2020). Role of new plant breeding technologies for food security and sustainable agricultural development. Applied Economic Perspectives and Policy, 42(2), 129–150. 10.1002/aepp.13044
22.
RahmanS. (2009). Whether crop diversification is a desired strategy for agricultural growth in Bangladesh?Food Policy, 34(4), 340–349. 10.1016/j.foodpol.2009.02.004
23.
RamK. A., TsunekawaA., SahaD. K., & MiyazakiT. (1999). Subdivision and fragmentation of land holdings and their implication in desertification in the Thar Desert, India. Journal of Arid Environments, 41(4), 463–477. 10.1006/jare.1999.0495
24.
RamaswamiB., & MurugkarM. (2016). Incremental reforms in food policy: What are the possibilities? In DevS. Mahendra & BabuP. G. (Eds.), Development in India: Micro and macro perspectives (pp. 125–153). Springer. 10.1007/978-81-322-2541-6_8
25.
RashidF. N. (2021). Achieving SDGS in Tanzania: Is there a nexus between land tenure security, agricultural credits, and rice productivity?Resources, Conservation and Recycling, 164, 105216. 10.1016/j.resconrec.2020.105216
26.
RoyD., & ThoratA. (2008). Success in high value horticultural export markets for the small farmers: The case of Mahagrapes in India. World Development, 36(10), 1874–1890. 10.1016/j.worlddev.2007.09.009
27.
SidhuS. S., & BaananteC. A. (1979). Farm‐level fertilizer demand for Mexican wheat varieties in the Indian Punjab. American Journal of Agricultural Economics, 61(3), 455–462. https://www.jstor.org/stable/1239431
28.
SinghP. A., & NarayananK. (2016). How can weather affect crop area diversity? Panel data evidence from Andhra Pradesh, a rice growing state of India. Studies in Agricultural Economics, 118(2), 91–100. 10.22004/ag.econ.246260
29.
WangJ., CramerG. L., & WailesE. J. (1996). Production efficiency of Chinese agriculture: Evidence from rural household survey data. Agricultural Economics, 15(1), 17–28. 10.1016/S0169-5150(96)01192-9
30.
WangJ., MendelsohnR., DinarA., & HuangJ. (2010). How Chinese farmers change crop choice to adapt to climate change. Climate Change Economics, 1(3), 167–185. https://www.adaptation-undp.org/sites/default/files/downloads/wang-mendelsohn-china-adaptation.pdf
31.
WilsonP., HadleyD., RamsdenS., & KaltsasI. (1998). Measuring and explaining technical efficiency in UK potato production. Journal of Agricultural Economics, 49(3), 294–305. 10.1111/j.1477-9552.1998.tb01273.x
32.
WordofaM. G., HassenJ. Y., EndrisG. S., AwekeC. S., MogesD. K., & RorisaD. T. (2021). Adoption of improved agricultural technology and its impact on household income: A propensity score matching estimation in eastern Ethiopia. Agriculture and Food Security, 10(1), 5. 10.1186/s40066-020-00278-2
33.
ZhangQ., DongW., WenC., & LiT. (2020). Study on factors affecting corn yield based on the Cobb–Douglas production function. Agricultural Water Management, 228, 105869. 10.1016/j.agwat.2019.105869
