Conference Agenda
Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
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Daily Overview |
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Egg-Timer: Climate Change and Agriculture 1 Location: D-111 Session Chair: Gaurav Arora, Indraprastha Institute of Information Technology | |
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Evaluating the role of mixed-cropping for managing production risks on small farms: An application of BetaIV framework for input-conditional crop yield density estimation 1: Indraprastha Institute of Information Technology, Delhi, India; 2: Indian Institute of Science, Education and Research, Bhopal, India Climate change and the resulting increase in crop failures pose significant production risks, particularly for smallholder farmers dependent on agriculture as their primary income source. Growing multiple crops is regarded as an important resilience strategy, enabling risk diversification through both inter-seasonal practices (e.g., double or triple cropping) and intra-seasonal approaches like mixed cropping. This study examines the role of mixed cropping - the simultaneous cultivation of multiple crops on a single plot - in production risk management among smallholder farms in semiarid and tropical regions in India. From a farm management perspective mixed cropping is expected to support higher farm incomes, improved dietary diversity, and lower production costs. However existing research exploring its farm productivity and production risk impacts is limited, inconclusive and predominantly based on agronomic experimental data. Here we investigate the effects of a cotton-pigeonpea traditional mixed cropping system on farm productivity and production risk. We employ beta regressions in conjunction with a non-linear instrumental variable framework to estimate input-conditional yield densities using plot-level primary survey data (Arora et al. 2021). The welfare implications of mixed cropping are measured using certainty equivalent - the expected income minus the cost of risk exposure. To estimate risk exposure, we use higher-order moments of the yield distributions, adapting the framework developed by Di Falco and Chavas (2009) to our multiple cropping context. We find that mixed cropping reduces the variability of returns and increases skewness, leading to an overall reduction in downside risk exposure despite its slight negative effect on mean returns. To our best knowledge, we provide the first piece of evidence on the role of mixed cropping cotton-legumes in managing farm-level production risk in India. JEL Codes: Q12, O13, C14, C36 Keywords: Yield Density Estimation; Risk Influencing Inputs; Beta Regression; Irrigation and Nitrogen Application; Instrumental Variables | |