Onion in Nashik: Cost of Cultivation, Price Spread and Lasalgaon Mandi Price Volatility (2014–2024, R)

This M.Sc (Agriculture) thesis in Agricultural Economics studies the economics of onion — India's most politically sensitive vegetable — in Nashik district, Maharashtra, the heart of the country's onion belt and home to the Lasalgaon APMC, widely described as Asia's largest onion market.

The thesis has three linked parts:

1. Cost of cultivation and returns from a primary survey of 120 onion growers selected by multistage random sampling across two talukas, computed with the CACP cost concepts (A1, A2, A2+FL, B1, B2, C1, C2, C3) separately for kharif, late-kharif and rabi onion, and by farm-size class.
2. Marketing channels, price spread and marketing efficiency — tracing the farmer's rupee from farm gate through commission agents, traders and wholesalers to the retail consumer, and measuring efficiency with Shepherd's and Acharya's modified measures.
3. Price behaviour at Lasalgaon (2014–2024) — monthly arrivals and modal prices from Agmarknet: trend, seasonal indices, Cuddy–Della Valle instability index, the arrival–price relationship, ARIMA forecasting and a GARCH(1,1) model of price volatility, all in R, with OPSTAT used for basic tests.

A Garrett ranking of production and marketing constraints closes the analysis. The bundle provides the research design, sampling frame, schedule structure, cost formulas, R workflow, thesis chapter plan, 12-slide presentation and viva bank. It contains no price or cost figures — every number must come from your own survey and downloaded series.

Abstract

Onion prices in India swing sharply between gluts, when farmers dump produce, and shortages, when retail prices make headlines and trigger export curbs. Farmers bear most of this risk. This study estimates the cost of cultivation and returns of onion across seasons and farm sizes in Nashik district, analyses marketing channels, price spread and efficiency, and models the level, seasonality and volatility of onion prices at Lasalgaon APMC over 2014–2024 using Agmarknet data. Primary data from 120 farmers are analysed with CACP cost concepts; time-series data with seasonal indices, instability indices, ARIMA and GARCH models in R. The findings are expected to inform storage, staggered marketing and policy decisions.

Introduction

Maharashtra is the leading onion-producing state and Nashik the leading district. Onion is grown in three seasons — kharif, late kharif and rabi — with rabi onion stored in chawls and released over months. Price outcomes depend on arrivals, storage decisions, rainfall shocks and trade policy such as export restrictions and minimum export prices.

Review gap

Earlier studies have estimated onion cost of cultivation or analysed price trends, but they usually treat one side alone, stop before the recent decade of repeated policy interventions, or use annual averages that hide the within-year volatility farmers actually face. Few combine season-wise CACP costs from farm data with monthly volatility modelling at the reference market for the same district.

Hypotheses

• Returns over cost C2 differ significantly across seasons and farm-size classes.
• The producer's share in the consumer rupee is higher in shorter channels.
• Monthly modal prices at Lasalgaon exhibit significant seasonality and time-varying volatility (ARCH effects).

Feasibility

• Data: Agmarknet provides free daily arrivals and prices for Lasalgaon; the survey is feasible in one rabi season through the SAU's research station and farmer networks.
• Tools: R and OPSTAT are free; Excel is available.
• Time: 26 weeks across Semesters 3–4, including outline approval, survey, analysis and thesis writing.

Onion farmers in Nashik invest heavily in seed, fertiliser, irrigation and hired labour, yet the price they receive at harvest can vary several-fold between seasons and years. Without season-wise cost estimates, it is not clear whether cultivation is profitable over cost C2 for small and marginal farmers or only for larger farmers who can store and wait. Without evidence on marketing channels and price spread, it is also unclear how much of the consumer's rupee reaches the farmer and where margins accumulate.

At the market level, sudden price spikes and crashes at Lasalgaon influence prices across India, but the combination of seasonality, arrivals and volatility clustering over the recent decade has not been modelled in a way that farmers, FPOs and extension agencies can use. This thesis estimates cost and returns from a primary survey, measures price spread and marketing efficiency, and models monthly price behaviour at Lasalgaon to identify when and how farmers can reduce price risk.

In scope

• Nashik district; two talukas with the highest onion area (for example Niphad and Yeola), four villages per taluka, 120 farmers.
• Kharif, late-kharif and rabi onion for the reference agricultural year.
• Monthly arrivals and modal prices at Lasalgaon APMC, January 2014 – December 2024, from Agmarknet.
• CACP cost concepts, price-spread analysis, time-series econometrics in R.

Out of scope

• Onion seed production and export-market price transmission.
• Retail-price surveys outside the district's retail centre.
• Causal policy evaluation of individual export notifications (they are noted as events, not modelled with a formal intervention design).
• Other APMCs beyond a descriptive comparison.

Research design

A descriptive-analytical design combining a cross-sectional farm survey with secondary time-series analysis.

Sampling (primary data)

Multistage random sampling:

1. District: Nashik, purposively (highest onion area).
2. Talukas: two with the highest onion area.
3. Villages: four per taluka, randomly from villages with substantial onion area.
4. Farmers: 15 per village (120 total), selected randomly from the village list after classifying into marginal (< 1 ha), small (1–2 ha), medium (2–4 ha) and large (> 4 ha), proportional to class size.

Market intermediaries: 10 commission agents, 10 traders/wholesalers and 10 retailers for price-spread data.

Analytical tools

Timeline (26 weeks)