Read our paper on the response of food supply chains in India during the pandemic. We conduct food group wise analysis of food expenditures & agricultural markets. We show which dimensions of supply chain matter. Collaborators: Nikita Gupta and Abhishek Shaw.
https://onlinelibrary.wiley.com/doi/10.1002/aepp.13365
Agriculture in the Inclusive Development Tenet/ Priority
Agriculture in the ‘Reaching the Last Mile’ Priority
Agriculture in the ‘Infrastructure and Investment’ Priority
Agriculture in the ‘Unleashing the Potential’ Priority
Agriculture in the ‘Green Growth’ Priority
Allocated Expenditure: Analysis
Figure 1: Proportion of Total Expenditure Allocated to Ministry of Agriculture and Farmers Welfare (%), 2004-05 to 2023-24
Note: Budget Estimates for all years have been considered.
Source: Expenditure Profile, Union Budgets 2004-05 to 2023-24
Figure 2: Budget Estimates for Expenditure on Major Items, 2023-24 in Comparison with Revised Estimates, 2022-23
Source: Expenditure Profile, Union Budget 2023-24
Figure 3: Expenditure of the Ministry (Total and PM-KISAN)
Source: Expenditure Profile, Union Budget 2014-15-2023-24
Source: Expenditure Budget, Union Budget 2023-24
Source: Expenditure Budget, Union Budget 2023-24
Table 1: Key Schemes -A Comparison of BE 2022-23, RE 2022-23 and BE 2023-24
| Central Sector Schemes/Projects | |||||
| Department of Agriculture and Farmers Welfare | Name of Scheme | Budget Estimates 2022-23 | Revised Estimates 2022-23 | Budget Estimates 2023-24 | % Change in Allocation from Revised Estimates 2022-23 |
| Pradhan Mantri Fasal Bima Yojana | 15500.00 | 12375.76 | 13625.00 | 10.10 | |
| Distribution of Pulses to State / Union Territories for Welfare Schemes | 9 | 166.21 | 800.00 | 381.31 | |
| Pradhan Mantri Kisan Samman Nidhi (PM-Kisan) | 68000.00 | 60000.00 | 60000.00 | No change | |
| Formation and Promotion of 10,000 Farmer Producer Organizations (FPOs) | 500.00 | 955.00 | 955.00 | No change | |
| Agriculture Infrastructure Fund (AIF) | 500.00 | 150.00 | 500.00 | 233.33 | |
| Market Intervention Scheme and Price Support Scheme (MIS-PSS) | 1500.00 | 1500.00 | 0.01 | -99 | |
| Pradhan Mantri Annadata Aay Sanrakshan Yojna (PM-AASHA) | 1.00 | – | 0.01 | – | |
| Department of Agricultural Research and Education | Agricultural Extension | 243.72 | 243.72 | 327.00 | 34.56 |
| Management of Natural Resources* | 185.77 | 185.77 | 240.00 | 29.19 | |
| Agricultural Education | 1995.83 | 1995.61 | 2423.41 | 21.87 | |
Source: Expenditure Budget, Union Budget 2023-24
Note: Management of Natural Resources includes Natural Resource Management Institutes including Agro Forestry Research and the Climate Resilient Agriculture Initiative. The Climate Resilient Agriculture Initiative will be merged with the Natural Resource Management Institutes including Agro Forestry Research scheme with effect from financial year 2023-24.
| Centrally Sponsored Schemes | |||||
| Department of Agriculture and Farmers Welfare | Name of Scheme | Budget Estimates 2022-23 | Revised Estimates 2022-23 | Budget Estimates 2023-24 | % Change in Allocation from Revised Estimates 2022-23 |
| Rashtriya Krishi Vikas Yojna | 10433.00 | 7000.00 | 7150.35 | 2.14 | |
| Krishionnati Yojana | 7183.00 | 5000.00 | 7066.47 | 41.32 | |
Source: Expenditure Budget, Union Budget 2023-24
There has been lots of discussion about farm, farmers and their income. Here, we bring a case of Sahyadri farms which proves the power of cooperation, market linkages and technology to empower farmers. This is exceptional story of an enterprise which shows the way forward for small holder agriculture.
One can access the case from the following link
https://cases.iima.ac.in/index.php/sahyadri-farms-a-21st-century-farmers-enterprise.html
#sahyadrifarms #agriculture #farmerproducercompany #agriculturetechnology #agriculturemarkets #farmincomes #FPO #IndianAgriculture
Introduction
Silk exports from India have increased after hitting a six-year low in 2021-22, with an increasing demand for silk carpets and garments in the USA and Europe. India is the only country in the world that produces all five varieties of commercial silk, i.e., Mulberry, Tropical Tussar, Oak Tussar, Eri, and Muga with a combined global market value of $16.86 Bn.
Problem
India, despite being the second-largest producer of silk (~35,000 MT), is ranked as the number one importer of raw silk, whereas it is the 9th largest exporter of raw silk in the world. India is the second-largest producer of raw silk as well as the largest consumer of raw silk and fabric. The unfulfilled demand is dependent on imports from China, Vietnam, Uzbekistan, Brazil, and the United Arab Emirates.
Observations
The Indian supply chain sector of silk is a highly unorganised and decentralised chain of activities. Activities are scattered cluster-based and community-based, and it has impacted the quality and quantity of export. India is mired by domestic challenges like use of outdated manufacturing technology, primitive and unscientific “reeling” and “weaving” techniques, use of poor-quality seeds, low production of bivoltine seeds, use of nongraded and diseased seeds, poor knowledge of farm disease amongst farmers, poor supply chain management, highly unorganized and decentralized sector, high production cost, and recurring droughts.
Analysis
As per the data collected through primary and secondary research, silk has high export demand, and India generates around 200 million USD annually by exporting silk and silk products. Mainly exported items include readymade garments, followed by silk fabrics, silk waste, silk carpets etc. There is a decrease in the value of silk exported from India. The reasons can be attributed to intense competition from China as less production of quality silk from India.
To analyse the productivity factors and the impact of varied factors on the export potential, the production data was taken for the last five years for all the states under silk production. Assuming silk production depends on the GDP of the state, the change in GSDP for that period, the wage rate of farmers, no. of silk farmers & reelers in the state and the fund allocated by the Govt. for silk development, data analysis was performed.
Silk production= f (GSDP, % change in GSDP, Wage rate, % of silk farmers, % of reelers, Govt. investment)
The correlation analysis shows that an increase in wage rate has a strong negative correlation with production, i.e., an increase in wage rate decreases production. An increase in GSDP has increased production, but after a certain time, it has a negative effect. It might happen due to alternative livelihood practices adopted by the silk farmers.
As per regression analysis, production is mostly affected by the wage rate. An increase in wage rate decreases production. Reeling has no significant effect on production, which shows that the processing & production of silk are not integrated. It is an identified gap in the silk value chain. The correlation analysis also shows a negative effect of public investment on silk production. This may be the result of leakage in the chain or diversion of the fund towards personal use by the farmers.
According to the silk demand and production trend, it is fairly observed that there is a gap in domestic silk production. For example, in 2021-22, India imported 1377 MT of raw silk. The trend analysis of silk production across various states shows a decline in production except in some north-eastern states like Assam, Meghalaya etc.
As per the data of the global market, there is a huge opportunity for export into Canada, Turkey, Vietnam, Japan, EU etc. The market demand is more than the total supply of silk. In Malaysia, the trade barriers are quite low, which allows India to make Malaysia as a major importer of Indian silk. Though global market shows attractive prospects for export, Indian reelers are unable to capitalize on the opportunity due to importing countries’ quality requirements and domestic problems like lack of transport & storage facilities, poor information on market trends, and lack of finance.
Recommendations
To combat domestic challenges and export quality silk, India has to increase public investment in the sector, renovate existing infrastructure, infuse networking services to quash information asymmetry, increase collectivization and farmers clusters pan India, primary processing near production facilities, ensure uniform quality by setting a rigid homogenous standard under “Silk Mark” through quality control, testing, & certification, and finally engage in “Make in India” branding. The government must raise the customs tax up from 20% and strictly condemn dumping of cheap silks to India to protect the indigenous reelers.
The existing policies & initiatives like FCA, RKVY, STEP, SRC, SAMARTH, TSP, SILKS Portal, e-Cocoon, SILK Mark must be revitalized in favour of the sericulture industry after understanding the nuances at a microscopic lens. Only if there is an overall development in the sector, there would be less attrition due to wage rate increase as the sector can attract and retain labour.
In the few thousand years that homo sapiens have existed, we have learnt to do and established our control over many great things, invention of the wheel, discovery of fire, the industrial revolution is certainly the most talked about milestones in human history. But perhaps the most important feature enabling the success of our species is the ability to produce food, having learnt a skill that we now call agriculture.
To master this skill and control the process, humans slowly began to understand and control the factors of production. Early humans understood well that water is the most important factor in production and thus emerged the idea of irrigation. The earliest form of irrigation probably involved people carrying buckets of water from wells or rivers to pour on their crops. As better techniques developed, societies-built irrigation canals, dams, dikes, water storage facilities drippers and sprinklers.
In India, the area under irrigation saw a major jump during the 1960’s. Primarily fuelled by public sector projects building dams and canals in combination with numerous wells and tube wells dug privately around the same time. Micro-irrigation till date accounts for 20% of the total irrigated area in the country.
Source: https://iasscore.in/data-story/irrigation-in-india
Multiple studies have discussed the state-to-state disparity in the development of micro-irrigation, financial viability, and challenges in adaptation of micro-irrigation systems. While researchers have extensively worked on the factors influencing micro irrigation adaptation, the focus has mostly been on terrain, ecosystem or cropping pattern related variables. Our study is unique in attempting to find correlation between adaptation of micro-irrigation to variables that capture the development on agricultural markets and demographics of the region along with other traditional variables such as annual rainfall or groundwater levels.The case in point studies the adaptation of micro-irrigation across the districts of Gujarat.
Methodology
The adaptation of Micro-irrigation across district was defined numerically as below:
With a level of significance (Alpha) of 0.10 simple linear regression was performed between the dependent variable (MI adaptation index) and 8 different independent variables.
| Table: 1 Summary of the linear regression model | |
| Dependent variable | Independent variables |
| MI adaptation index | Avg. annual rainfall (mm) |
| Avg. groundwater level (mbgl) | |
| % Literacy | |
| Sex ration (females per 1000 males) | |
| Ag. Market infrastructure (proxy: # of functional APMCs) | |
| Per capita milch animal population | |
| No. of electrified ag. Wells per village | |
| % of villages with regional water supply | |
| Level of significance | 0.90 of 90% |
Results
Of all the 8 independent variables assumed to have a significant relationship influencing the adaptation of micro irrigation, only two hold a statistically significant relationship. The quality and strength of market infrastructure (# of functional APMCs) in the district and the coverage of regional water supply across villages of the district have the most significant influence of the rate of micro irrigation adaptation in the district. Graphs 1 shows the scatterplot and regression derived trendline equation relating market development and MI adoption index.
The results emerging above once again highlight the importance of an efficient and functioning market for agriculture produce. The R-sq value of 0.26 indicates that as much as 26% of the variation in adaptation of MI across different districts in Gujarat is explained just by the level of market development. The negative correlation between the variables indicate that MI adaptation is higher where markets are underdeveloped. In fact, In districts with less than 0.01 functional APMCs per village the adaptation of micro irrigation is as high as 5 times the identified potential for the district.
This indicates that most of the current MI adoption in the state is not driven by production improvement effort, but it is rather a stress induced forced adoption by farmers in order to sustain the system of production.
This stress or market forced adoption of MI is a major reason for poor financials returns on such systems for farmers across the country. Further, since these forced adopters of technology are unable to set an attractive precedent in most cases, other farmers do not buy the idea, which in turns limits the adoption of micro-irrigation systems in areas where it can be most profitable not just financially but also ecologically.
Believe it or not every small investment decision the farmer makes is strongly linked to the market for his/her produce. It is important to appreciate the strong link the markets hold with almost all factors of production. The need of the hour is for policy makers (and other stakeholders) to appreciate this interlinked system and fine-tune technology adoption incentives accordingly.
Note: This write up is based on a project done by Jayant and team under Prof. Vidya Vemireddy’s guidance.
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Vidya Vemireddy