| Literature DB >> 33270687 |
Shweta Gupta1, Avinash Kishore2, Muzna Fatima Alvi1, Vartika Singh1.
Abstract
India has one of the largest agricultural input support programs in the world, delivered in the form of subsidies to farmers, raising concerns about its sustainability. This paper evaluates the performance of one such support, the micronutrient subsidy program in the state of Andhra Pradesh (AP) and presents a case for providing this support in the form of direct cash transfers. Under the program, key soil micronutrients- zinc, boron, and gypsum were distributed free of cost to farmers living in micronutrient-deficient areas, with identification and targeting managed entirely by the state. We survey 1621 farmers, 61 agriculture extension officers, and 78 agriculture input dealers to assess the efficacy of the program and to identify bottlenecks preventing effective targeting, with a focus on zinc. We find that use of non-subsidized zinc is high in AP, and awareness of benefits of zinc and physical access to input dealer shops are significant predictors of zinc use. We argue that the free provision of micronutrients may have created demand among farmers, but there is little justification to continue subsidizing such a program at such high rates or resorting to public distribution. We find that micronutrient procurement and distribution has become a burden on extension staff and crowds out the private sector. Our analysis shows that the subsidy can benefit more farmers if it is channeled through the network of private fertilizer dealers. We use administrative data on budgetary outlays and digital soil maps to suggest fiscal redistribution in the form of direct cash transfers that may ensure more effective targeting at a lower cost to the state.Entities:
Year: 2020 PMID: 33270687 PMCID: PMC7714421 DOI: 10.1371/journal.pone.0242161
Source DB: PubMed Journal: PLoS One ISSN: 1932-6203 Impact factor: 3.240
Annual budget for micronutrient subsidy.
| Year | Zinc sulfate distributed (metric tons) | Total area covered (in hectares) | Total subsidy value (in INR) |
|---|---|---|---|
| 2014–15 | 3,600 | 287,545 | 122 million |
| 2015–16 | 6,833 | 751,390 | 298 million |
| 2016–17 | 6,613 | 818,077 | 484 million |
| 2017–18 | 13,465 | 1,273,205 | 865 million |
| 2018–19 | 7,922 | 730,426 | 703 million |
Source: [8]
Fig 1Soil zinc status (ppm) and the quantity of free zinc distributed in Andhra Pradesh, 2017–18.
Source: Authors calculations. Shapefiles were accessed from open-access source GADM (https://gadm.org/index.html). The districts are numbered as follows: 1 = Chittoor, 2 = Anantapur, 3 = Kadapa, 4 = Nellore, 5 = Kurnool, 6 = Prakasam, 7 = Guntur, 8 = Krishna, 9 = West Godavari, 10 = East Godavari, 11 = Vishakhapatnam, 12 = Vizianagaram, 13 = Srikakulam. Panel A shows the district-wise zinc status in parts per million (ppm). These numbers are calculated using Soil Health Data from the second cycle (2017–18). Panel B shows the district-wise supply of zinc in 2017–18, in kg per hectare of the area covered. Area covered is a sum of area under paddy, maize, cotton, and other crops. These numbers are calculated using data from the zinc trials conducted by CIMMYT in 2017–18.
Descriptive statistics of sample households.
| Characteristics | Mean (Sd)/ Proportion |
|---|---|
| Age (years) | 50.23 (12.35) |
| Male (%) | 94.2 |
| Has some formal education (%) | 60.7 |
| Land owned (acres) | 2.30 (2.53) |
| Cultivated in Kharif 2018 (%) | 96.55 |
| Cultivated in Rabi 2018–19 (%) | 41.21 |
| Land cultivated in Kharif 2018 (acres) | 3.26 (4.71) |
| Land cultivated in Rabi 2018–19 (acres) | 3.64 (6.36) |
| Tenant farmer (%) | 30.04 |
| Primary crop Paddy in either season (%) | 81.55 |
| Applied zinc in either season (%) | 44.91 |
| Applied boron in either season (%) | 3.89 |
| Applied gypsum in either season (%) | 13.33 |
| Can identify zinc deficiency in soil (%) | 46.88 |
| Owns a Soil Health Card (%) | 11.66 |
| Caste: | |
| General Caste (%) | 36.64 |
| Scheduled Caste (%) | 11.23 |
| Scheduled Tribe (%) | 7.4 |
| Other Backward Class (%) | 44.73 |
Note: Standard deviation in parentheses
Fig 2Reasons for non-usage of zinc.
Source: Authors’ calculations.
Determinants of zinc usage.
| Variables | Used zinc in 2018–19 | Used zinc in 2018–19 |
|---|---|---|
| (only paddy farmers) | ||
| Zinc Awareness = 1 | 0.340 | 0.366 |
| (-0.0277) | (-0.0309) | |
| Land owned (acres) | 0.0310 | 0.0298 |
| (-0.0116) | (-0.013) | |
| Land owned squared (acres) | −0.00176 | −0.0019 |
| (-0.001) | (-0.001) | |
| Tenant = 1 | 0.0878 | 0.0781 |
| (-0.0321) | (-0.0331) | |
| Educated = 1 | 0.0628 | 0.0484 |
| (-0.0256) | (-0.0294) | |
| Used Boron = 1 | 0.315 | 0.334 |
| (-0.0533) | (-0.0615) | |
| Used Gypsum = 1 | 0.0121 | 0.123 |
| (-0.0402) | (-0.0476) | |
| Distance from nearest shop (km) | −0.0115 | −0.0128 |
| (-0.0039) | (-0.00426) | |
| Cultivated Paddy in 2018–19 = 1 | 0.117 | – |
| (-0.0358) | ||
| District Fixed Effects | Yes | Yes |
| R- squared | 0.235 | 0.232 |
| Number of observations | 1620 | 1321 |
Note: Standard errors in parentheses
***, **, and * denote significance at 1%, 5%, and 10%, respectively. We also controlled for farming experience (years), ownership of SHC data, interaction with MPEOs, and caste fixed effects. Standard errors are clustered around villages.
Fig 3Zinc application rates for free zinc users vs. purchased zinc users.
Source: Authors’ calculations.
Determinants of zinc quantity.
| Variables | Used Zinc in 2018–19 |
|---|---|
| Used only purchased zinc = 1 | −4.08 |
| (-0.61) | |
| Used both purchased and free zinc = 1 | −3.50 |
| (-2.01) | |
| Cultivated in only Kharif = 1 | 4.48 |
| (-1.42) | |
| Cultivated in both Kharif and Rabi = 1 | 4.15 |
| (-1.46) | |
| Cultivated Paddy = 1 | −1.88 |
| (-0.75) | |
| Zinc status of the village (ppm) | 0.27 |
| (-0.23) | |
| Farming experience (years) | 0.16 |
| (-0.07) | |
| Farming experiencesq (years) | −0.002 |
| (-0.001) | |
| Tenant = 1 | −.39 |
| (-0.67) | |
| Irrigation = 1 | 1.92 |
| (0.66) | |
| Marginal farmer = 1 | −.87 |
| (-0.84) | |
| Small farmer = 1 | 0.56 |
| (-0.98) | |
| Semi-medium farmer = 1 | −.50 |
| (1.14) | |
| Medium farmer = 1 | 0.05 |
| (1.39) | |
| District Fixed Effects | Yes |
| R- squared | 0.216 |
| Number of observations | 720 |
Note: Standard errors in parentheses
***, **, and * denote significance at 1%, 5%, and 10%, respectively. We also controlled for ownership of SHC, caste fixed effects, and gender and education status of the farmer.
Fig 4Concerns of extension staff.
Source: Authors’ calculations.
Fig 5Troubleshooting methods used by extension staff.
Source: Authors’ calculations.
Fig 6Major sources of information used by farmers.
Source: Authors’ calculations.