Post-harvest Management and Farm Outcomes

Over the past two decades or so, India’s food system has been transitioning with a declining demand for cereals and pulses and ­increases in the demand for high value horticulture and livestock products with rising incomes, urbanisation, and female labour force participation. Farmers, in this demand-led system, however, have not ­received commensurate benefits from this transition, leading to an incre­asing rural–urban divide. Value chains remain poorly developed, in terms of storage ­facilities at the farm level, transportation, and food processing units, especi­ally in case of the perishables. Due to this, farmers incur higher losses compared to wholesalers, processors and ­retailers. Indian agriculture seems to have hit the frontier in intensive margin.

Over time, the government has introduced several schemes to lift the rural economy. The agriculture sector, in the recent years, has seen successive central- and state-promoted schemes like Mantri Kisan Samman Nidhi, Natio­nal Agriculture Market (eNAM), Pradhan Mantri Krishi Sinchayee Yojana, and Pradhan Mantri Fasal Bima Yojana to boost farmers’ outcomes. In 2016, the Government of India also stated its goal of doubling farmers’ income by 2022.

Notwithstanding the policy thrust, the agriculture sector has, by and large, shown a lackluster performance with a meagre 2.9% growth rate during 2014–15 to 2018–19 (GoI 2019–20). Farmers’ inco­mes have fallen progressively below that of the non-farm sectors. Most farmers in India remain stuck in a low-income trap. In 2015–16, about 68% farmers with marginal landholdings earned an annual income of `33,636 from farming, which translates into a monthly income of `2,803, which is barely one-fifth of the national average (GoI 2015–16). Further, between 2014 and 2016 period, the farm revenue fell by 6% per year because of low market prices (OECD/ICRIER 2018).

While several reasons—ranging from fragmentation of landholdings, lack of public investment, plateauing of technological gains, to underdeveloped and fragmented agricultural markets—frequently come up as the factors contributing to farmers’ low incomes, an important aspect that receives rather inadequate attention in the literature is the role of post-harvest management (PHM) in determining farmer income.

Much of the existing literature on PHM focuses on its role in reducing post-­harvest losses, which indeed affect farmers’ returns. However, in mitigating these losses, PHM works in more complex ways that go beyond merely arresting post-harvest losses. By empowering farmers in choosing the time and location of sales, influencing the product quality, fostering product differentiation and, hence, better price discovery, PHM plays an important role in both value generation as well as value distribution.

What Is PHM?

After production, agricultural produce undergoes a series of post-harvest operations, handling stages and storage ­before they reach the consumers. Each post-harvest stage results in some losses and has an effect on the value distribution. These are the factors that determine the gap between consumer and farmer prices of a product.

Post-harvest food is one of the many direct ways in which value distribution between the consumer and producer is affected. This is defined as the measurable quantitative (decreased weight or volume) and qualitative (unwanted cha­nges in the cosmetic features of food and reduced nutrient value) losses along the supply chain that can occur at any stage, starting from the time of harvest till the end uses (Hodges et al 2011; De Lucia and Assennato 1994; Buzby and Hyman 2012). Such losses can reduce the value of the produce below its market value (Hodges et al 2011).

An assessment of crop losses conducted by the Indian Council of Agricultural Research in 2016 revealed that about 3.9% to 6% cereals, 4.3% to 6.1% pulses, 2.8% to 10.1% oilseeds, 5.8% to 18.1% fruits, and 6.9% to 13% vegetables were lost during harvesting, post-harvesting activities, handling and storage (Jha et al 2016). On the other hand, as per the estimates of the Committee on Doubling Farmers’ Income (2019), at the all-India level, farmers are unable to sell about 40% of the total fruits and vegetables produced in the market or lose around `63,000 crore every year for not being able to sell their produce for which they have already made investments (Pandey 2018).

With about 80% of the Indian farmers being small and marginal, the post-­harvest losses have first-order effects on them. Beyond post-harvest losses, poor storage facilities compel smallholder farmers in India to sell their produce at low prices soon after the harvest. On the other hand, quality and quantity losses due to poor storage, of particularly the high value crops, has possibly been the major contributor of low farmers’ ­income and seasonal food deficits at the household level. Simultaneously, with storage facilities per se and PHM in general, the possible stranglehold of the middlemen and traders can be weakened by empowering the farmers in terms of their choice of markets.

Case of TARINA and PHM

To address the issue of losses and weak bargaining power of the farmers, the Tata-Cornell Institute, under its flagship project Technical Assistance and Research for Indian Nutrition and Agriculture (TARINA), introduced post-harvest interventions in various states of India, namely Bihar, Odisha and Uttar Pradesh (UP). The study explored various pathways to examine the impact of PHM interventions on farmers’ welfare and how it is reflected in farmers’ choices and outcomes. The objective was to move away from the technical issue of food losses per se to economic losses, while understanding that the former is nested in the latter.

Data and methodology: The intervention was carried out in four districts, namely Munger in Bihar, Kandhamal and Kalahandi in Odisha, and Maharajganj in UP. There were 450 households in the treatment group and 450 households in the control group in each of the four districts of the three states. It was ensured that, on an average, the household characteristics of both the groups were similar, and that the treatment and control groups reacted similarly to the intervention. For instance, among other indicators, the socio-economic profile of the households was considered. Both the treatment and control groups fared similarly on these indicators and therefore would have had similar responses to the intervention.

A baseline survey of 3,600 households was conducted in 2017 and an end line survey of 3,275 households, with an approximately 10% attrition rate, was conducted in 2019 to assess the impact of the intervention. Attrition was due to ­either migration, or households not available during the time of the surveys. There was no random assignment of beneficiaries, in order to assess the causal relationships, and a double difference propensity score matching (PSM) across two time periods has been used in the study. The analysis also includes multivariate regressions on the matched sample.

As part of the intervention, households who were a part of the treatment group were provided with PHM techno­logies and demonstration on application of these technologies. Many households were given training on best practices for cleaning, sun-drying and storing of grains. These trainings were like the Krishi Vigyan Kendra’s archetype, where farmers are provided with trainings and on-field demonstrations about the latest technologies in agriculture.

Different types of PHM technologies were distributed to households depen­ding upon their farm produce. Some households were provided with a single technology while some were given a combination of technologies. Largely distributed PHM technologies included grain-pro bags or hermetic bags, with a combined storage capacity of 70–75 kilogram (kg) per bag of crops, such as pulses (chickpea and lentils) and wheat. In some locations, multigrain threshers (both power and pedal operated) and spiral graders for cleaning and grading of pulses and grains were also introduced. This was done to assist the farmers in creating value for their produce right at the farm level. Other PHM technologies and equipment included moisture meters, metallic storage bins, solar driers, and vegetable stands among others. A total of 13 PHM technologies and equipment were distributed to households in the four intervention districts of TARINA.

Storage Loss Matters

With the focus on storage practices as part of post-harvest intervention, Table 1 presents the economic effects of post-harvest losses—the reduction of which is the quintessential target of PHM. Most of the post-harvest technology (PHT) that we examine in our project is related to storage. Hence, the point that we emphasise here is that storage loss has material consequence, and thus forms the basis for our storage-based post-harvest intervention. The questions that we explore are: how important is storage? and, what is the importance of storage failure that would make a case for storage-based intervention?

Here, in terms of farmer outcomes, we examine the household expenditure as a measure of welfare. Since we are examining the rural households, variability in income and its sources mandates proxying household income with household expenditure. Taking household expenditure as a marker of household welfare, in two commodities, that is, green gram as well as paddy, with storage loss, there is significant reduction in household ­income (proxied by expenditure) (Table 1). Hence, there is a basis for PHM from a welfare standpoint. In per capita monthly expenditure, post-harvest loss is asso­ciated with a reduction in household expenditure to the tune of `317 per individual, that is, about `1,600 at the household level assuming an average family size of five.

The interaction term is the variable capturing the impact of storage. Asse­ssed as the impact of storage on the treatment group (income/expenditure of the treated farmers), conditional on storing in the end-line period, the interaction term captures the treatment effect on the treated.

PHM and Farmers’ Outcomes

Since storage loss has a material consequence for the farmers, we examined its effect on the price realisation of the farmers. How do the farmer outcomes differ in terms of improving the bargaining power across space and time?

An a priori assumption is that with improved PHT like storage, farmers can store optimally with flexibility in marketing in terms of when to sell, thus supplying the produce as and when the market demands. This can minimise distress selling in which the farmers get a raw deal. PHT thus helps farmers to explore markets and optimise the timing of sales to get optimum prices. This, thus, translates into better price realisation and higher income.

The impact of PHM can be gauged by comparing treated farmers with the control farmers. With two periods of data, we use double difference PSM and regression on the matched sample to assess the impacts on household incomes. Notwithstanding the roles played by crop-specific factors in estimating an impact and the fact that it does not work uniformly for all crops, in Table 2, we present the estimate of causal impacts for only three commo­dities, namely, wheat, pigeon pea and ­potato. Given that storage leads to better price discovery, we find that household expenditure is higher for those who adopt storage-based PHM practices in the case of wheat, ­pigeon pea and potato.