Data Sources

Indicators

• Energy (kcal)
• Carbohydrates (g)
• Protein (g)
• Fat (g)
• Iron (mg)
• Calcium (mg)
• Zinc (mg)
• Sodium (mg)
• Cholesterol (mg)
• Total Free Sugars (g)
• Total Saturated Fatty Acids (mg)
• Total Mono Unsaturated Fatty Acids (mg)
• Total Poly Unsaturated Fatty Acids (mg)
• Total Dietary Fiber (g)
• Folic Acid (µg)
• Vitamin A/retinol (µg)
• Vitamin B1/thiamine (mg)
• Vitamin B2/riboflavin (mg)
• Vitamin B3/niacin (mg)
• Vitamin B5/pantothenic acid (mg)
• Vitamin B6/pyridoxine (mg)
• Vitamin B7/biotin (µg)
• Vitamin B9/folate (µg)
• Vitamin C/ascorbic acid (mg)
• Total Carotenoids (µg)

Data Sources

The Indian National Food Sampling and Analysis Programme

Institution Who Collected Primary Data

National Institute of Nutrition (NIN)

Indian Council of Medical Research (ICMR)

Nutrition Research Laboratories (NRL)

Department of Health Research

Ministry of Health & Family Welfare

International Network of Food Data Systems (INFOODS) at the Food and Agriculture Organization of the United Nations provides assistance in terms of standards development and capacity building.

Years Data Have Been Collected

IFCT, also known as Nutritive Value of Indian Foods, contains data that was sampled, analyzed and categorized on a massive scale after 1971 (after 45 years).

In 2011, ICMR initiated a new ICFT as an authoritative source of food composition data in India.

Years Included in Poshan Atlas

2017

Population Subgroups Available

The following table summarizes the crops with production data as matched to IFCT nutrient data. Note that for aggregate categories of “other” crops such as “other cereals” and “other oilseeds” as well as “small millets,” nutrient values are not given.

Methods of Data Collection

IFCT 2017 includes nutrient information for 528 raw foods. Values are derived from composite food samples representing the average of six geographic regions across India (Figure 1).

Figure 1. Map of food sample collection centers and six regional zones. From: page xiv of IFCT 2017 report.

The food sampling methodology was developed in order to generate reliable data representing the composition of the foods of interest (Figure 2). The sampling procedure to generate the national food composition database for key foods involves selecting self-weighing, nationally representative estimates of the composition of foods. Overall, 107 districts out of the total 630 districts representing 17% of the country are selected for the collection of key foods for analysis, selected according to probability-proportional to size. A protocol was developed for collection and handling of food samples in order to optimize the sample integrity and thereby the stability of nutrients.

Figure 2. Data flow. From: page xxiii of IFCT 2017 report.

Official methods of analysis of the Association of Official Analytical Chemist (AOAC) or if no AOAC method was available, other appropriate and reliable analytical methods that have undergone collaborative evaluations were tested and adopted. Each method was standardized in the laboratory and validated as per the IUPAC/AOAC.

Example of Interpreting Data

The iron content of horse gram (Macrotyloma uniflorum) is 8.80 mg per 100 g as compared to arhar/tur/pigeonpea/red gram (Cajanus cajan) which is 5.37 mg per 100 g.

References and Further Reading

IFCT 2017 including details regarding the measurement of each nutrient : https://drive.google.com/file/d/1eqQ578gHiPoIaHaVYjQa_3sFe_LzGhm1/view

Indicators

Area and Production Statistics (APS):

• Area Cropped in Kharif and Rabi (000 ha)
• Production in Kharif and Rabi (MMT)
• Yield in Kharif and Rabi (kg/ha)
• Area Cropped in Rabi(000 ha)
• Production in Rabi (MMT)
• Yield in Rabi (kg/ha)
• Area Cropped in Kharif (000 ha)
• Production in Kharif (MMT)
• Yield in Kharif (kg/ha)
• Top 10 cultivated crops by Area

Village Dynamics in South Asia (VDSA):

• Area Cropped in Kharif and Rabi (000 ha)
• Production in Kharif and Rabi (MMT)
• Yield in Kharif and Rabi (kg/ha)
• Top 10 cultivated crops by area

Data Sources

2017-2018: APS

1966: VDSA

Institution Who Collected Primary Data

APS:

Directorate of Economics and Statistics (DES)

Department of Agriculture, Cooperation and Farmers Welfare

Ministry of Agriculture and Farmers Welfare

For crop yield, Field Operations Divisions of the National Statistical Office (NSO), Ministry of Statistics & Programme Implementation, provides technical guidance to the States/Union Territories for organizing and conducting Crop Estimation Surveys. In addition, NSO, in collaboration with States/Union Territories, implements sample check programs under the Scheme for Improvement of Crop Statistics. Results are published in the NSO report, “Consolidated Results of Crop Estimation Surveys on Principal Crops.”

VDSA:

DES records compiled by International Crops Research Institute for the Semi-Arid Tropics (ICRISAT)

Years Data Are Available

APS: Annual data since 1997-1998 are available online from the Department of Agriculture, Cooperation and Farmers Welfare. An agricultural crop year is July to June. Final estimates are released in January/February of the following agricultural year.

VDSA: Annual from 1966-1967 to 2011-2012

Years Included in Poshan Atlas

APS: 2017-2018

VDSA: 1966-1967

Population Subgroups Available

Data are available by crop type. The crops available for 2017 APS data are summarized below:

The crops available for 1966 data from the VDSA database are summarized below:

Missing data

2017:

The lists above should be interpreted in light of the fact that not all crops – even some that are grown widely such as rice – have data available for some states. The number of crops with data available in 2017 varied from 0 in four Union Territories (Dadra and Nagar Haveli, Delhi, Ladhak, and Lakshadweep) to 51 in Andhra Pradesh.

1. No APS data on major crops grown are available for the states of Odisha and Kerala (with the exception of minor pulses)

2.No Horticulture data are available for the states of Goa, and Sikkim, and the Union Territories of Dadra and Nagar Haveli, and Daman and Diu

3. No APS data are available for the Union Territories of Dadra and Nagar Haveli, Delhi, Ladhak and Lakshadweep

1966:

For the year 1966, the VDSA database includes crop area, and production statistics for 311 districts across 19 states (Andhra Pradesh, Assam, Bihar, Chhattisgarh, Gujarat, Haryana, Himachal Pradesh, Jharkhand, Karnataka, Kerala, Madya Pradesh, Maharashtra, Odisha [formerly Orissa], Punjab, Rajasthan, Tamil Nadu, Uttar Pradesh, Uttarakhand, West Bengal). 1966-1967 data in the VDSA database are from DES (see pp. 87-88 of this PDF: http://vdsa.icrisat.ac.in/Include/document/all-apportioned-web-document.pdf ).

1. Data for small millets and other (minor) cereals, and other (minor) oilseeds are not available

2.Among cash crops, Cotton (lint) is the only fiber crop for which data are available (Jute, Mesta, and Sunhamp data are not available) and there are no data on tobacco a

3. Excluding Potato, Onion, and total area under fruits and vegetables, no disaggregated data on all other horticulture corps are unavailable

Special note on Madhya Pradesh data

2017 data for Madhya Pradesh is obtained from the state Horticulture department for all crops, including non-horticulture crops. Data is available only for the full year, and not for Kharif and Rabi separately. To ensure that data are compatible with APS available for all other states, the following changes were made:

1.Paddy has been renamed to “Rice”

2.Tur has been renamed to “Arhar/Tur”

3.Teora has been renamed to “Khesari”

4.Kulthi has been renamed to “Horse gram”

5.Area under “Kodo-Kutki” has been added to area under “small millets”

6.“Peas” have been added to area under “Peas & beans (pulses)”

7.Area under “Lentil” and “Other lentils” have been added to “Other pulses”

For the last three changes, area and production data for both categories were added, and yield was calculated as production divided by area.

Top 10 crops by district:

Top 10 food-crops by area are calculated for each district from both datasets: 2017 APS published by the Ministry of Agriculture and Farmers Welfare; 1966 data compiled by VDSA. Fiber crops, namely Cotton (lint), Jute, Mesta, and Sunhamp, and Tobacco were removed from the 2017 data, and Cotton (lint) was removed from the 1966 data, to retain only food-crops.

VDSA data is available from 1966-2011, with data available for district boundaries as of 2011. The process used by VDSA to match data from old districts to new can be found on pp. 71 of the document above. For comparison of ‘top 10 crops’, current districts (as of December 2019), are matched to districts from the VDSA database. In case changes in district boundaries, top 10 crops of districts that were formed entirely from one ‘parent’ district are compared to crops grown in the ‘parent’ district. For example, Nirmal district of Telangana is formed from the erstwhile Adilabad district of Telangana. Hence, top 10 crops grown in Nirmal today, are compared to top 10 crops grown in Adilabad district in 1966. For each district displayed in the Poshan Atlas, the matching VDSA district name and 1966 base district name (per VDSA) can be found in Annexure A here .

As noted above, top 10 crops by district must be interpreted in light of missing data. As data for “small millets”, “other cereals”, and “other oilseeds” are unavailable from 1966, there can be no direct comparision of these categories between the two timepoints.As disaggregated data for “other pulses”, “fruits”, and “vegetables” is unavailable from 1966, area under certain crops from APS data was summed in order to makecomparable to 1966 data:

The following pulses from the APS 2017 dataset were to make comparable to “Minor pulses” area as available from VDSA 1966

1.Cowpea (Lobia)

2.Guarseed

3.Horse Gram

4.Khesari

5.Masoor

6.Moong

7.Moth

8.Other Pulses

9.Peas & beans (pulses)

10.Urad

The following fruits from the APS 2017 dataset were to make comparable to “Fruit” area as available from VDSA 1966

1.Aonla

2.Apple

3.Banana

4.Citrus

5.Grapes

6.Guava

7.Mango

8.Papaya

9.Pineapple

10.Pomegranate

11.Sapota

12.Strawberry

13.Muskmelon

14.Watermelon

The following vegetables from the APS 2017 dataset were to make comparable to “Vegetables” area as available from VDSA 1966

1.Beans

2.Bottle Gourd

3.Brinjal

4.Cabbage

5.Capsicum

6.Carrot

7.Cauliflower

8.Cucumber

9.Green Chilli

10.Okra

11.Peas

12.Radish

13.Sweet Potato

14.Tapioca

15.Tomato

Methods of Data Collection

APS:Data on area cultivated are collected via three different schemes:

1. Timely Reporting Scheme (TRS)

18 States and 4 Union Territories (Andhra Pradesh, Assam [excluding hilly districts], Bihar, Chandigarh, Chhattisgarh, Dadra & Nagar Haveli, Delhi, Gujarat, Haryana, Himachal Pradesh, Jammu & Kashmir, Jharkhand, Karnataka, Madhya Pradesh, Maharashtra, Puducherry, Punjab, Rajasthan, Tamil Nadu, Telangana, Uttar Pradesh, and Uttarakhand).

Crop area and land use statistics are part of the land records maintained by the revenue agency (commonly known as patwari agency). Previously, a complete enumeration of all fields was made in every village during each crop season to compile land use, irrigation, and crop area statistics. Since the 1970s, however, a random sample of 20% of villages in the State is selected in such a way that during a period of five years, the entire State is covered.

2.Establishment of an Agency for Reporting of Agricultural Statistics (EARAS)

6 States (Arunachal Pradesh, Kerala, Nagaland, Odisha, Sikkim, Tripura, and West Bengal).

No revenue agency at the village level. Crop area and land use statistics are collected through a scheme of sample surveys. A random sample of 20% of villages in the State is selected in such a way that during a period of five years, the entire State is covered.

3. “Conventional” Estimates

5 States and 3 Union Territories (Andaman & Nicobar Islands, Assam [hilly districts], Daman & Diu, Goa, Lakshadweep, Manipur, Meghalaya, and Mizoram).

5 States and 3 Union Territories (Andaman & Nicobar Islands, Assam [hilly districts], Daman & Diu, Goa, Lakshadweep, Manipur, Meghalaya, and Mizoram).

Improvement of Crop Statistics SchemeUnder this scheme, an independent agency of supervisors carries out a physical verification of the data in a subsample of the sample villages and makes an assessment of the extent of discrepancies between the supervisor’s and patwari’s crop area entries in the sample clusters. Discrepancies between the patwari’s and supervisor’s crop entries have been noted in about one-third of the survey numbers inspected, with discrepancies being large in some cases. Thus, some caution should be taken in interpreting findings. This is especially true for short-duration crops such as vegetables. Reference:http://www.mospi.gov.in/42-crop-area-statistics

Yield estimates of crops are obtained through crop cutting experiments conducted under General Crop Estimation Surveys (GCES). Stratified multi-stage random sampling design is generally adopted for carrying out GCES with tehsils/ taluks/revenue inspector circles/blocks/anchals, etc., as strata, revenue villages within a stratum as first-stage sampling unit, survey numbers/fields within each selected village as second-stage sampling unit, and experimental plot of a specified shape and size as the primary sampling unit.

In each selected primary sampling unit, generally 2 survey numbers/fields growing the experimental crop are selected for conducting crop cutting experiments. Generally, 80-120 experiments are planned in a major crop growing district and 44-46 experiments in a minor growing district. 5 The number of experiments allotted to a district is distributed among the strata within the district roughly inproportion to the area under the crop in the stratum.

The estimates of crop production are obtained by multiplying area estimates by corresponding yield estimates.

Example of Interpreting Data

In 1966, the top 10 crops by cultivated area in Kurnool district of Andhra Pradesh were:

1.Jowar

2.Groundnut

3.Rice

4.Bajra

5.Minor Pulses (includes all pulses other than Gram and pigeonpea)

6.Arhar/Tur

7.Ragi

8.Castorseed

9.Fruits

10.Gram

In 2017, the top 10 food crops in Kurnool district of Andhra Pradesh had changed to:

1.Gram

2.Groundnut

3.Arhar/Tur

4.Rice

5.Vegetables

6.Maize

7.Onion

8.Minor Pulses (includes all pulses other than Gram and pigeonpea)

9.Castorseed

10.Bajra

References and Further Reading

APS data: https://aps.dac.gov.in/APY/Public_Report1.aspx

APS data: http://vdsa.icrisat.ac.in/vdsa-requestData.aspx

Weekly state-wise updates on sowing: http://agricoop.nic.in/all-india-crop-situation

Indicators (units)

• Depth to groundwater (m bgl)
• Decadal mean water level fluctuation pre-monsoon (percent rise)
• Decadal mean water level fluctuation pre-monsoon (percent fall)
• Decadal fluctuation - percent wells recording fall of 0-2 m
• Decadal fluctuation - percent wells recording fall of 2-4 m
• Decadal fluctuation - percent wells recording fall of > 4 m
• Decadal fluctuation - percent wells recording rise of 0-2 m
• Decadal fluctuation - percent wells recording rise of 2-4 m
• Decadal fluctuation - percent wells recording rise of > 4 m
• Number of wells analyzed per district

Data Sources

India-WRIS (Water Resources Information System)

India-WRIS was initiated by a Memorandum of Understanding signed on December 3rd, 2008 between the Ministry of Jal Shakti and the Indian Space Research Organization (ISRO), Department of Space. It is managed by the National Water Informatics Centre (NWIC).

Institution Who Collected Primary Data

Central Ground Water Board (CGWB)

Department of Water Resources

Ministry of Water Resources, River Development & Ganga Rejuvenation (now Ministry of Jal Shakti)

Jointly with State Groundwater Departments: http://cgwb.gov.in/StateGW-Departments.html

Years Data Are Available

1993 to 2019

Data are reported as either annual fluctuations or decadal fluctuations. For decadal fluctuations, means are only derived if water level data are available for at least 6 of the selected 10 years. Only decadal fluctuations are included in the Poshan Atlas.

Years Included in Poshan Atlas

2019, as compared to mean pre-monsoon water level for 2009 to 2018

Population Subgroups Available

None

Methods of Data Collection

Groundwater levels are monitored four times a year through a network of 23,196 monitoring wells (Figure). Wells are found in all states except Mizoram & Sikkim and the Union Territory of Lakshadweep. The four time points each year include: January, pre-monsoon (March/April/May), August, and post-monsoon. Only pre-monsoon values are included in the Poshan Atlas because these are the standard for scenario reports published by CGWB.

Figure. Location of groundwater monitoring stations. From “Ground Water Scenario in India, Pre-monsoon 2018” published by CGWB.

Example of Interpreting Data

The groundwater level data for pre-monsoon 2019 indicated a maximum depth to water level of 75.71 m bgl in Gandhinagar district of Gujarat and minimum of 0.9 m bgl in North and Middle Andaman district of Andaman and Nicobar Islands.

A comparison of depth to water level of pre-monsoon 2019 with the decadal mean of pre-monsoon (2009-2018) indicates that of 22 wells analyzed in North West Delhi, 45% are showing a rise in water level and 54% are showing a decline in water level. In terms of the amount of rise, 41% are showing an increase of 0-2 m and 5% an increase of 2-4 m. In terms of the amount of decline, 18% are showing a decline of 0-2 m, 27% a decline of 2-4 m, and 9% a decline of > 4 m.

References and Further Reading

Source of data (must first register for a free account) : https://indiawris.gov.in/wris/#/DataDownload

Groundwater scenario reports : http://cgwb.gov.in/GW-Scenario.html

In addition to groundwater level, regular in-depth assessments of recharge from rainfall and other sources (monsoon and non-monsoon season), total natural discharge, extraction for irrigation, industrial use, and domestic use, and the ultimate state of groundwater extraction are available. These in-depth assessments were conducted in 1995, 2004, 2009, 2011, 2013, and 2017. The 1995 assessment used the Ground Water Estimation Committee (GEC) - 1984 methodology. The 2004, 2009, 2011, and 2013 assessments used the GEC - 1997 methodology. The 2017 assessment used the GEC - 2015 methodology. A total of 6,881 units were assessed in 2017.

Indicators (units)

• Percent gross cropped area treated with pesticide
• Percent gross cropped area treated with fertilizer
• Total fertilizer consumption - NPK (000 tonnes)
• Total quantity of fertilizer consumed - Nitrogenous (000 tonnes)
• Total quantity of fertilizer consumed - Phosphatic (000 tonnes)
• Total quantity of fertilizer consumed - Potash (000 tonnes)
• Total pesticide consumption (tonnes)

Data Sources

Input Survey:

1.Percent gross cropped area treated with pesticide

2.Percent gross cropped area treated with fertilizer

Agriculture Statistics at a Glance:

3.Total fertilizer consumption - NPK (000 tonnes)

4.Total quantity of fertilizer consumed - Nitrogenous (000 tonnes)

5.Total quantity of fertilizer consumed - Phosphatic (000 tonnes)

6.Total quantity of fertilizer consumed - Potash (000 tonnes)

Directorate of Plant Protection, Quarantine & Storage:

7.Total pesticide consumption (tonnes)

Institution Who Collected Primary Data

Input Survey:

Agriculture Census Division

Department of Agriculture, Cooperation and Farmers Welfare

Ministry of Agriculture and Farmers Welfare

Technical staff from the District Statistical Office, Taluk Statistical Office, and Directorate of Agriculture collect primary data.

Fertilizer Consumption:

Department of Fertilizers

Ministry of Chemicals and Fertilizers

Pesticide Consumption:

State Agriculture Departments

Compiled by the Pesticide Monitoring and Documentation Unit

Directorate of Plant Protection, Quarantine, and Storage

Department of Agriculture, Cooperation and Farmers Welfare

Ministry of Agriculture and Farmers Welfare

Years Data Have Been Collected

Input Survey:

Every four years, from 1976-1977 to 2011-2012

Fertilizer Consumption:

Unknown

Pesticide Consumption:

Unknown

Years Included in Poshan Atlas

Input Survey:

2011-2012

Fertilizer Consumption:

2016-2017 to 2018-2019

Pesticide Consumption:

2016-2017 to 2018-2019

Population Subgroups Available

Input Survey indicators are available by size-group, but not compiled in the Poshan Atlas.

Methods of Data Collection

Input Survey:

Input Survey follows the Agriculture Census exercise. The survey covers the whole country. All types of agricultural holdings, except institutional holdings and holdings operated by persons not residing in the village, are enumerated. Thus, only individual and joint holdings operated by resident cultivators constitute the population for the 2011-2012 survey. 13 The reference point for the survey is 2011-2012 and data was collected from 1st July 2012, after the completion of the 2011-2012 crop season.

A two-stage stratified sampling methodology was used for the Input Survey 2011-2012. Tehsils/Blocks constituted the strata, villages within a stratum the first-stage units, and ‘ Operational Holdings ’ in the selected villages the second-stage units. The sample size of first-stage units was 7% of the total number of villages from each stratum. These 7% villages were selected randomly out of the villages already selected for Phase-II of Agriculture Census, 2010-2011. Operational holdings were sub-divided into five size groups and a simple random sample of four operational holdings was selected from each of the five size groups 14 . If in a selected village, the total number of operational holdings was four or less in a particular size-group, then all of the holdings for that size group were selected. Data was collected through household surveys of the selected operational holders.

Schedule 2.2 of the Input Survey collects data on irrigation status, area treated with one or more fertilizers, quantity of fertilizer used, by cultivated crop type. An operational holding may be treated wholly, partly, or not treated at all with fertilizers. For the purpose of the survey, holdings were classified only as treated or not treated with fertilizers. Accordingly, partly treated holdings were also considered as treated with fertilizer. Area treated with fertilizer, as reported by the survey, has been divided by Gross Cropped Area to calculate percent gross cropped area treated with fertilizer for the Poshan Atlas.

Schedule 2.6 collects data on pest management practices under the following categories:

• Agronomic and cultural practices
• Mechanical control
• Biological, nature based or environmental methods
• Chemical methods
• Others (none of the above 4)
• No effort

Total area managed with chemical methods is divided by the Gross Cropped Area to calculate percent gross cropped area treated with pesticides for the Poshan Atlas.

Fertilizer Consumption:

Data are compiled by the Department of Fertilizers, Ministry of Chemicals and Fertilizers (previously under the Ministry of Agriculture Cooperation and Farmer’s Welfare).

Pesticide Consumption:

The Pesticides Unit of the Directorate compiles data received during Zonal Conferences for Kharif and Rabi Seasons each year, Department of Chemicals & Petrochemicals and Directorate General of Statistics & Commercial Intelligence, Kolkata. The Pesticides Unit monitors the demand and availability of pesticides in States/UTs for adoption of crop protection measures. With coordination by the Statistics Unit, it also collects and compiles various data on Demand & Consumption of pesticides, Sale Points for the distribution of pesticides in the country, etc. State level data are presented at seasonal States/ UTs Zonal Conferences on Inputs by respective states and compiled by the Pesticides Unit.

Consumption estimates are collected for select chemical (Technical Grade) pesticides, presented in tonnes at the state level. District level consumption data are currently unavailable.

Data from Andhra Pradesh, Telangana, Orissa, and Punjab have been revised based on data presented in the respective state parliaments. Specific details are found in “ CONSUMPTION OF CHEMICAL PESTICIDES IN VARIOUS STATES/UTs DURING 2014-15 TO 2019-20”

Example of Interpreting Data

In 2011, only 23.67% of gross cropped area in Srikakulam district of Andhra Pradesh was treated with pesticides, compared to 76.38% in Krishna district, the highest in the state.

From the 2016 to 2018, total fertilizer use in the state of Tamil Nadu increased from 908,450 tonnes to 1,129,300 tonnes. In the same period, pesticide use fell from 2,092 tonnes to 1,901 tonnes in the state.

References and Further Reading

Input Survey 2011-12 report : http://agcensus.nic.in/document/is2011/is2011rep.html

Department of Plant Protection statistical database : http://ppqs.gov.in/statistical-database

Pesticide Monitoring and Documentation Unit : http://ppqs.gov.in/divisions/pesticides-monitoring-documentation/pesticide-monitoring-and-documentation-unit

Indicators

• The total population of major species (number)
• The total population of cattle (number)
• The total population of buffalo (number)
• The total population of sheep (number)
• The total population of goat (number)
• The total population of pig (number)
• The total population of other livestock (number)
• The total population of poultry (number)
• Total production of milk (tonnes)
• Total production of eggs (number)
• Total production of meat (tonnes)

Data Sources

Two schemes:

• Livestock Census - Total population of livestock species
• Integrated Sample Survey - Total production

Institution Who Collected Primary Data

Animal Husbandry Statistics Division

Department of Animal Husbandry and Dairying

Ministry of Fisheries, Animal Husbandry, and Dairying

Indian Agricultural Statistics Research Institute (IASRI) is a technical advisor to the Integrated Sample Survey.

Years Data Have Been Collected

Annual since 2012

Years Included in Poshan Atlas

March 2018 - February 2019

Population Subgroups Available

Livestock Census indicators (population) are available by breed, gender, age, utility (within species), and by urban-rural ownership, but not compiled in the Poshan Atlas.

Methods of Data Collection

The Livestock Census is a complete count of the livestock and the poultry in the year 2018-2019.

The data are collected from March to February in three seasons comprising four months each: summer season, rainy season, and winter season. The survey covers 5% of villages each season in both rural and urban areas, which accounts for a total of 15% of villages (approximately 96,000 villages in a year). The sampling is organized into three stages: first-stage units are villages or urban wards, second-stage units are households, and third-stage units are animals.

The survey is conducted in two stages: Complete Enumeration (which gives the estimated livestock population) and Detailed Survey (gives the average production).

- For Complete Enumeration, the 5% ward selection happens using simple random sampling without replacement for every district for each season. However, this might vary according to the size of the district and the availability of the staff. To measure the standard error, these 5% wards are further divided into two sub-samples and the production estimates are derived from these sub-samples separately.

- For Detailed Survey, the selection of sample villages is done by selecting four villages and one urban ward from each sub-sample to estimate the production rate for the rural and urban areas separately. Annually, at the beginning of every survey period, the State/UTs select the sample villages and wards.

In the first month of every season, the enumerator collects the data to list all livestock (including poultry) in each household in the selected villages. Based on this listing, the total milk animals are estimated for milk production and total estimated layers in case of egg production, for meat production, the total number of birds/animals slaughtered and the number of sheep shorn in case of wool production. In the case of meat production, to estimate the number of animals/birds slaughtered, slaughterhouses, poultry farms, and butcher shops are also surveyed.

There are Eight Schedules to cover all the information mentioned above:

Schedule 1 - General information

Schedule 2 - Complete enumeration

Schedule 3 - Details of milk production in selected households

Schedule 4 - Details of egg production in selected households

Schedule 5 - Details of egg production in the commercial poultry farms

Schedule 6 - Details of wool production in sample households

Schedule 7 - Information on meat production from the recognized slaughterhouse

Schedule 8 - Details of broilers and layers’ meat production in the commercial poultry farms

Interpreting Data and an Example

As per the listing, the total milk animals are estimated in the case of milk production. The milk-producing animals are cattle, buffalo, and goat.

The largest producer of milk is Uttar Pradesh with 30518.9 tonnes which are 16.3% of the total production of milk in the country followed by Rajasthan and Madhya Pradesh. Dadra and Nagar Haveli and Lakshadweep contribute the smallest amount of milk by giving 1.01 and 3.66 tonnes, respectively.

References and Further Reading

Livestock Census :  http://dadf.gov.in/sites/default/filess/20th Livestock census-2019 All India Report.pdf

Basic Animal Husbandry Statistics : http://www.dahd.nic.in/about-us/divisions/statistics

Instruction Manual :  http://dadf.gov.in/sites/default/filess/Instruction manual Final %2813.01.2015%29 2_0.pdf

Indicators

• Soils deficient in nitrogen (percent)
• Soils classified as “very low” in nitrogen (percent)
• Soils classified as “low” in nitrogen (percent)
• Soils classified as “medium” in nitrogen (percent)
• Soils classified as “high” in nitrogen (percent)
• Soils classified as “very high” in nitrogen (percent)
• Soils deficient in phosphorus (percent)
• Soils classified as “very low” in phosphorus (percent)
• Soils classified as “low” in phosphorus (percent)
• Soils classified as “medium” in phosphorus (percent)
• Soils classified as “high” in phosphorus (percent)
• Soils classified as “very high” in phosphorus (percent)
• Soils deficient in potassium (percent)
• Soils classified as “very low” in potassium (percent)
• Soils classified as “low” in potassium (percent)
• Soils classified as “medium” in potassium (percent)
• Soils classified as “high” in potassium (percent)
• Soils classified as “very high” in potassium (percent)
• Soils deficient in sulphur (percent)
• Soils deficient in boron (percent)
• Soils deficient in copper (percent)
• Soils deficient in iron (percent)
• Soils deficient in manganese (percent)
• Soils deficient in zinc (percent)
• Soils deficient in organic carbon (percent)
• Soils classified as “very low” in organic carbon(percent)
• Soils classified as “low” in organic carbon (percent)
• Soils classified as “medium” in organic carbon (percent)
• Soils classified as “high” in organic carbon (percent)
• Soils classified as “very high” in organic carbon(percent)

Data Sources

Soil Health Card Scheme

Institution Who Collected Primary Data

Integrated Nutrient Management (INM) Division

Ministry of Agriculture and Farmers Welfare

The Department of Agriculture, Cooperation and Farmers Welfare providesguidance in technical matters.

Various of laboratories are involved:

– Mini Soil Testing Laboratories at Village level by VillageEntrepreneurs.

– At the Soil Testing Laboratory (STL) owned by the Department ofAgriculture and by their own staff.

– At the STLs owned by the Department of Agriculture but by the staffof the outsourced agency.

– At the STLs owned by the outsourced agency and by their staff.

– At Indian Council of Agricultural Research (ICAR) Institutionsincluding Krishi Vigyan Kendras (KVKs) and State AgriculturalUniversities (SAUs).

– At the laboratories of the Science Colleges/Universities by thestudents under the supervision of a Professor/ Scientist.

Years Data Have Been Collected

Annual since 2015-2016

Years Included in Poshan Atlas

2017-18 to 2018-19 Cycle

Population Subgroups Available

None

Methods of Data Collection

Villages are selected using a stratified sampling methodology. Samplesare collected from marginal, small, and large farms to addressvariations that arise due to different management practices. Within eachfarm size class, samples are chosen to represent all possible soilfertility variations.

The following 12 parameters are tested in soil samples:

• Major nutrients
• Nitrogen (N)
• Phosphorus (P)
• Potassium (K)
• Secondary nutrients
• Sulphur (S)
• Micronutrients
• Boron (Bo)
• Copper (Cu)
• Iron (Fe)
• Manganese (Mn)
• Zinc (Zn)
• Physical parameters
• Physical parameters
• Electrical conductivity
• Organic carbon

Soil samples are drawn in a grid of 2.5 ha in irrigated area and 10 hain rainfed area with the help of GPS tools and revenue maps. Soilsamples are taken generally two times in a year, after harvesting ofRabi and Kharif Crop, or when there is no standing crop in the field.The ideal time for collection of soil samples is between harvests of onecrop and sowing/planting of other crops when fields are vacant. Soilsamples are collected by a trained person from a depth of 15-20 cm bycutting the soil. It is collected from four corners and the center ofthe field and mixed thoroughly and a sample of this is picked up andtransferred to the soil test laboratory for analysis. Shaded areas areavoided. Details of the farmer, land, soil, GPS coordinates, etc. arerecorded. It will then be.

Criteria of samples to be taken:

(i) Irrigated area – at 2.5 ha grid for marginal and smallholdings

(ii) Irrigated area – one sample each holding for semi-medium,medium and large holdings

(iii) Rainfed area – at 10 ha grid for marginal, small,semi-medium and medium holdings

(iv) Rainfed area – one sample each holding for large holdings

Definition of “deficiency” :

Definition of very low, low, medium, high, and very high nutrientcontent:

Interpreting Data and an Example

In 2018, 51.55% of soils in the district of East Godavari, AndhraPradesh, are classified as deficit in Iron, while only 0.49% of soils inVizianagaram are deficit in Iron. However, only 4.34% and 1.99% of soilsin Vizianagaram are classified as “high” or“very-high” in Nitrogen respectively.

The largest producer of milk is Uttar Pradesh with 30518.9 tonnes which are 16.3% of the total production of milk in the country followed by Rajasthan and Madhya Pradesh. Dadra and Nagar Haveli and Lakshadweep contribute the smallest amount of milk by giving 1.01 and 3.66 tonnes, respectively.

References and Further Reading

Soil Health Card website : https://www.soilhealth.dac.gov.in/

Operational Guidelines for Implementation of the Soil Health CardScheme : http://mpkrishi.mp.gov.in/hindisite/pdfs/SHC.pdf

Data Processing for Poshan Atlas

For soil deficiency in terms of major nutrients, all "0“ weredisplayed as "NA“ as SHC report on deficiency of majornutrients (N, P, K, OC) does not differentiate between "0“and unavailable data.

Indicators

• Actual annual rainfall (mm)
• Normal annual rainfall (mm)

Data Sources

India-WRIS (Water Resources Information System)

India-WRIS was initiated by a Memorandum of Understanding signed on December 3rd, 2008 between the Ministry of Jal Shakti and the Indian Space Research Organization (ISRO), Department of Space. It is managed by the National Water Informatics Centre (NWIC).

Institution Who Collected Primary Data

Indian Meteorological Department (IMD) Grid

Years Data Have Been Collected

Annually since 1901

Years Included in Poshan Atlas

2018, 2019

Population Subgroups Available

Not applicable

Methods of Data Collection

District-wise average annual rainfall collected by the IMD grid is compiled in the Poshan Atlas.

Normal Annual Rainfall: Normal annual rainfall is the Long Period Average (LPA) calculated by the IMD based on a 50-year average for the period 1951-2000 for each administrative division, based on data collection from a network of 2,412 stations across India (Figure).

Actual Annual Rainfall: Actual annual rainfall is computed based on rainfall data from about 3,500 stations spread over the country. Based on daily rainfall data from these stations, the average annual rainfall of each district is computed.

Figure. Location of rain gauges for Normal Rainfall monitoring. From “Rainfall Statistics of India – 2018” published by the India Meteorological Department (Ministry of Earth Sciences).

Example of Interpreting Data

The actual annual rainfall data for 2019 indicated a maximum of 5,517 mm (compared to 3,320 mm normal annual rainfall) in Amravati district of Maharashtra, and minimum of 196 mm (compared to 376 mm normal annual rainfall) in Jhajjar district of Haryana.

References and Further Reading

Rainfall Statistics of India reports can be found here : http://hydro.imd.gov.in/hydrometweb/(S(ukc1vrehizdin3bmkdjgmrup))/landing.aspx

Details on IMD publications can be found here : http://www.imdpune.gov.in/library/publication.html