Varietal discrimination of Basmati rice in north- west India

A. N. Singh*, Dharmesh Verma** and M. H. Kalubarme *Global Institute of Land, Water and Environment Management, Lucknow, India ** United Phosphorous Ltd., India *** BISAG, Gandhinagar, India What is Basmati Rice? • Basmati is a premium long grained aromatic rice grown in a specific geo- environment , i.e. NW India and parts of Pakistan for centuries. Documentary evidences show that Basmati has been grown in this area for more than 250 years (Nene, 2003). • Its high value stems from its unique eating qualities, which includes aroma in both the raw and cooked state, kernel length 7 mm or more, excellent linear elongation on cooking almost double its kernel length, soft and flaky consistency of cooked rice. • Basmati 370, Taraori Basmati, Type3 and Ranbir Basmati are the Traditional Basmati varieties grown in Punjab, Haryana, western U.P. , Uttarakhand and J&K. These are tall (140-148 cm plant ht.),145-150 d growing period, kernel length varying from 6.9 to 7.3 mm, breadth 1.7 to 1.9 mm, kernel elongation ratio after cooking 1.8 to2.1 . Evolved Basmati varieties

• Pusa Basmati-1, Pusa-1121, CSR-30 and Pusa-1509 are the varieties evolved (recently bred) Basmati having traditional Basmati varieties as one of the parent. These varieties are being grown in a larger area now due to higher yield. • Factors favouring aroma in Basmati are cool temp. during flowering and grain development (25 degree C/ 31 degree C night/day temp. during crop maturity), use of organic manures, fertile, light-textured and well-drained soil, direct sowing, etc. Objectives of study

The study, commissioned by the Agricultural Products Export Development Agency (APEDA), Govt. of India, had the following objectives: • Remote Sensing data based area estimate of traditional and evolved Basmati varieties in Punjab (21 dist.), Haryana (20), western Uttar Pradesh (26), Uttarakhand (4), Jammu & Kashmir (2), and Himachal Pradesh (2). • CCE based yield prediction in different districts/regions. • Monthly report on Basmati during its cropping season – growth, crop condition, biotic and abiotic stresses • Annual change in area under basmati varieties. Study Area IRS-P6 AWiFS : 24-SEP-2008 Covering Haryana and Punjab States IRS-P6 AWiFS : 03-Oct-2010 Covering Haryana and Punjab States REMOTE SENSING OF CROP PHENOLOGY

An attempt was made to differentiate High yielding rice and basmati varieties based on their phonological stage differences which in turn have impact on spectral reflectance on the satellite data. In general, the high yielding rice varieties are sown and transplanted one month in advance of Basmati varieties in most of the study areas. The most important reason for late sowing and transplantation of basmati varieties is that these varieties Sharbati should mature during the cold night periods during late October or first week of November which helps to produce better aroma in the basmati. Basmati Seed to seed duration of commonly grown Basmati varieties Traditional Basmati • Basmati-386 / Taraori Basmati 150 d • Basmati-370 145 d Evolved Basmati • CSR-30 140 d • Pusa Basmati-1 140 d • Pusa-1121 130-135 d Methodology

Data Used • Multi-temporal IRS AWiFS (56m), LISS-III (23m) digital data for the rice growing period and Liss-IV (6m) for selected area. • Due to cloud cover, good quality data available were generally after 15 Sept. . AWiFS with 5 d repeat cycle provided more frequent

Field Data and GPS Measurements • Ground Truth (GT) was collected during fourth week of August to first week of October, which coincided with flowering to grain formation stage of rice crop. Agronomic data like variety, stage/vigor, and height of the crop canopy, soil exposure were recorded. Minimum size of plot considered was 300 * 300 sq. m. to collect data using GPS. Crop Cutting Experiments (CCE) for yield estimation in all the states. For example, in Haryana, CCE were conducted in 190 plots covering 10 districts. Selection of images for varietal study based on crop calender

• In Punjab, Traditional Basmati varieties (Basmati 386) is in flowering stage in last week of October and harvested in 4th week November. • Evolved Basmati (Pusa Basmati-1 and PB1121) flowers in 2nd week of October and harvested in 3rd week of November. • HYVs are harvested in 4th week of September. • In Haryana, transplanting of Basmati varieties is done about 15 days earlier than Punjab, and accordingly all crop stages. • In Uttar Pradesh, transplanting is similar to Punjab. • In Jammu & Kashmir, only Ranbir Basmati is grown, which is of shorter duration. • Hence, images of last week of September to 1st week of October and onwards were selected for analysis. Steps in IRS LISS-III Digital Data Analysis

• Geo-referencing • Administrative boundary superimposition • Generation of spatial information in GIS environment • Superimposing GPS locations of Basmati and high yielding rice varieties on the registered LISS-III digital data, • Identification of basmati and high yielding rice varieties on LISS-III digital data, • Supervised classification using MXL classifier with boundary mask approach, • Area estimation under different rice varieties • Generation of spectral vegetation indices like NDVI DN to Radiance Conversion • Calculation of at-sensor spectral radiance is the fundamental step in converting image data from multiple sensors and platforms into a physically meaningful common radiometric scale. • In order to obtain radiometrically comparable apparent spectral radiance data suitable for further processing, the integer digital number (DN) of each band of all images was transferred into real numbers using the spectral calibration data. The calibration was done by following expression of satellite spectral radiance Lλ, (Lillesand et.al; 2000) which is,

Lrad = {[DN/MAX GRAY] * [Lmax - Lmin]} + Lmin

Where, DN = Digital numbers of a pixel, Max grey: Maximum DN possible for a given data. Lmax and Lmin are the maximum and minimum radiance values for band (mWcm-2 Sr-1 µm1). Generation of Training Signatures and Separability Analysis • LISS-III and LISS-IV images of selected growth phases of major HYVs and Basmati varieties ( Last week of September onwards)were used. GPS based training sites were collected for different rice varieties and other land-use classes. • Five-to-six classes with different developmental stages and percent ground cover having different vigour for each rice variety were identified for training signature generation. • The training signatures contain multi-band statistics such as mean, standard deviation, and variance-covariance matrix for each class, which is used in supervised classification. • Spectral separability of basmati rice varieties and other HYVs were generated. • Before using these signature statistics in the supervised classification, the crop separability was studied by computing the Transformed Divergence for different classes. Spectral Reflecatnce of Rice Varieties 120 IRS LISS-III Spectral Bands Band 2-Green: 0.52 – 0.59 100 Band 3-Red: 0.62 – 0.68 Band 3-NIR : 0.76 – 1.55 – 1.70

80 Basmati-1 Basmati-2 Basmati-3 60 HYV-1

DN Values HYV-2 40 Sharbati-1 Sharbati-2

20

0 0.55 0.65 0.81 1.62 Central Wavelength (micro meter) IRS-P6 LISS-III images of two dates showing differentiation of Basmati from HYVs in part of Karnal district, Haryana

31-Aug-2008 24-SEP-2008

Basmati

High Yielding Varieties Sharbati 31-August 2008 24-September 2008 Traditional Basmati

Evolved Basmati

South-Western Part of Karnal District GPS points for Training sites & CCE Confusion Matrix

Percent Pixels Classified by Code Class Code No. Pixels 5 10 15 20 30 40 50 60 70 75 85 95 110 115 River sand 5 4787 83.79 13 0 0 0 0 0 0 0 0 0 3.18 0.02 0 Waste Land 10 567 4.59 93.83 0 0 0 0 0 0 0 0 0 1.23 0 0 Habitation 15 9545 3.41 42.38 0 0 0 0 0 0 0.05 0 0.17 44.83 1.32 0 Water 20 856 0 0 0 68.81 2.57 0 0 0 0 0 0 3.1 16.56 0 Water-2 30 1292 0 0.08 0.09 4.49 75.54 0 0.15 0 0 0 0 3.15 16.56 0 Basmati-1 40 6647 0 0 0 0 0 23.3 69.8 0 0 0.02 6.71 0.02 0 0 Basmati-2 50 3120 0 0 0 0 0 1.15 77.98 0.03 0.38 2.08 16.44 0.06 0 0 Sharbati-1 60 768 0 0 0 0 0 0 8.98 59.77 17.32 11.07 2.86 0 0 0 Sharbati-2 70 577 0 0 0 0 0 0 0.35 14.73 83.36 0 0.17 1.39 0 0 HYV-1 75 2991 0 0 0 0 0 0.33 4.55 0.74 0.3 62.29 31.16 0 0 0 HYV-2 85 1296 0 0.08 0 0 0 0 7.87 0 0.46 5.25 85.65 0.31 0 0 Fallow land 95 4461 3.12 0.81 5.04 0 0 0 0.87 0 3.18 0 0.11 78.7 0.99 5.87 waterlogged 110 3358 0.12 1.43 0.03 0.09 4.59 0 8.7 0 0 0 0.12 5.48 79.09 0 Other Veg. 115 584 0 0 0 0 0 0 1.2 0.17 0.34 0.15 0.35 5.6 0 92.29

Average accuracy = 82.81 % Overall accuracy = 91.58 % KAPPA COEFFICIENT = 0.9220 Signature Separability using Transformed Divergence

Separability Measure: Transformed Divergence Average Separability: 1994636 Minimum Separability: 1509900 Maximum Separability: 2.000000 Minimum Separability: Sharbati-1 and Sharbati-2

Class River Sand Waste Land Habitation Water-1 Water-2 Basmati-1 Basmati-2 Sharbati-1 Sharbati-2 HYV-1 HYV-2 Fallow Land Waterlogged Stream Waste Land 1.999966 Habitation 2.000000 2.000000 Water-1 2.000000 2.000000 2.000000 Water-2 2.000000 2.000000 2.000000 2.000000 Basmati-1 2.000000 2.000000 2.000000 2.000000 2.000000 Basmati-2 2.000000 2.000000 2.000000 2.000000 2.000000 1.981544 Sharbati-1 2.000000 2.000000 2.000000 2.000000 2.000000 2.000000 1.999990 Sharbati-2 2.000000 2.000000 2.000000 2.000000 2.000000 2.000000 2.000000 1.509900 HYV-1 2.000000 2.000000 2.000000 2.000000 2.000000 1.999995 2.000000 2.000000 2.000000 HYV-2 2.000000 2.000000 2.000000 2.000000 2.000000 1.999778 1.929037 2.000000 2.000000 1.950382 Fallow Land 2.000000 2.000000 1.999963 2.000000 2.000000 2.000000 2.000000 2.000000 2.000000 2.000000 2.000000 Waterlogged 2.000000 2.000000 2.000000 2.000000 1.999815 2.000000 2.000000 2.000000 2.000000 2.000000 2.000000 2.000000 Stream 2.000000 1.999966 2.000000 2.000000 2.000000 1.999966 2.000000 2.000000 2.000000 2.000000 2.000000 1.992781 2.000000 Other Veg 2.000000 2.000000 1.999966 2.000000 2.000000 2.000000 1.998234 1.999993 1.999996 2.000000 1.996616 1.999999 2.000000 1.999986

NDVI Image of IRS LISS-III data of 24-Sep-2008 covering Karnal Kaithal & Kurukshetra

KURUKSHETRA

KAITHAL

KARNAL Basmati and HYV variety Classification using NDVI Thresholding Crop Yield

• Crop yield data collected from CCE and Agriculture department from high yielding and basmati growing states in India, along with agro- meteorological data and Spectral Vegetation Index like Normalized Difference Vegetation Index (NDVI) was analyzed for developing zonal Agromet-Spectral –Yield models using multiple regression analysis. Relationship between CCE Yield and NDVI values of Basmati Variety in Karnal & Panipat Districts

Pusa Basmati: NDVI Vs. Yield (Panipat, Haryana) 48 47 46 45 44 43 42 41

Yield (q/ha) 40 39 Pusa Basmati: NDVI Vs. Yield (Karnal, Haryana) 38 56 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 54 NDVI Yield = 25.27*NDVI + 30.57 52 YIELD (q/ha) Linear (YIELD (q/ha)) R2 = 0.95 50 48 46

Yield (q/ha) 44 42 40 0.35 0.40 0.45 0.50 0.55 0.60 0.65 NDVI Yield= 45.15*NDVI + 25.23 Yield (q/ha) Linear (Yield (q/ha)) R2 = 0.91 Relationship between CCE Yield and NDVI values of Sharbati & Basmati Varieties

Sharbati Rice: NDVI Vs. Yield (Karnal, Haryana) Pusa-1121: NDVI Vs. Yield (Muzaffarnagar, UP) 60 56 59 54 58 52 57 56 50 55 48

54 Yield (q/ha) Yield (q/ha) 46 53 52 44 0.35 0.40 0.45 0.50 0.55 0.60 0.35 0.40 0.45 0.50 0.55 0.60 0.65 NDVI NDVI Yield = 45.66*NDVI + 34.61 Yield = 16.15*NDVI + 41.34 Yield (q/ha) Linear (Yield (q/ha)) 2 Yield (q/ha) Linear (Yield (q/ha)) R2 = 0.84 R = 0.67 Crop Cutting Experiments (CCE) for yield estimation

Crop Cutting Experiments were conducted using standard procedures in the study area for assessment of yield.

 The CCE derived yield was averaged for the district and a conversion factor used for offsetting the moisture content of the grain for estimation of district level production.

In Haryana State, Crop Cutting Experiments were conducted in 190 plots covering 10 districts. Based on CCE data, the range of productivity of different Basmati varieties computed is given in Table-1. Agro-met-spectral Yield Models

 Normalized Difference Vegetation Index (NDVI) of Basmati and high yielding rice varieties of a particular administrative district / tehsil for 10 crop seasons were generated

Meteorological data like rainfall, Tmax, Tmin, Relative Humidity (RH %), sunshine Hours etc. of previous 10-years have been collected form IMD for a particular Met Station.

The Basmati yield data at district/tehsil-level was also collected from the Department of Agriculture of the same periods.

Agro-meteorological yield models were generated using this data set and using the current seasons met data.

These Agro-meteorological yield models can be used for predicting the basmati yields well in advance of the harvesting period.

The crop condition term was also be incorporated into the yield models to take into account the yield reduction due yield reducing factors. An attempt was made to predict the Basmati rice yields using the Agro-meteorogical yield model. The observed and model predicted Evolved basmati yields for Karnal, Panipat, Kurukshetra districts are given in the following figure.

Rice Yields in Karnal District 3400

3200

3000

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2600 RICE YIELDRICE (kg/ha) 2400

2200 1994 1996 1998 2000 2002 2004 2006 2008 YEAR OBSERVED PREDICTED State-wise area under Basmati and other varieties Area in '000 ha Basmati-386, Evolved Basmati Non-Notified S. No. State Total Rice 370, Type-3 Pusa-1121 PB-1 CSR-30 Sharbati Sugandha 1 Punjab 2780.40 4.50 622.70 28.12 53.24 49.30 - 2 Haryana 1081.70 424.65 94.95 74.48 7.70 - 3 Uttar Pradesh 1550.00 18.05 289.85 59.00 - 112.90 70.35 4 Uttarakhand 143.00 8.20 7.00 1.70 - 33.40 3.30 5 Jammu & Kas hmir 96.00 34.00 8.50 - - - - 6 Himachal Pradesh 72.88 2.85 - - 40.17 - 7 Delhi 1.50 Total 5723.98 64.75 1357.05 183.77 127.72 243.47 73.65

Aromatic Indica Basmati Rice Acreage, Production and Export During Last One Decade 10000

9000 Aromatic Indica Basmati Rice Acreage, Production 10000 8000 and Export During Last One Decade 9000

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1000 0 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 YEAR 0 Area Production Export (000, tons) Value (million USD) 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 YEAR

Area Production Export (000, tons) Value (million USD) Conclusion

• Traditional and Evolved Basmati varieties were separable by proper selection of data base on phenology and analysis of IRS Liss-3 data. AWiFS data with 5d repeat cycle used in conjuction with Liss-3 proved helpful wherever data gap existed. • Within the two groups of Basmati, ground survey based fraction of diff. varieties was used to arrive at percent area under different varieties. • Crop cutting based yield was used to calculate the production of different varieties. NDVI and NDWI based yield model was also developed and used to validate the field data. • Abiotic stress, like flooding in Punjab in 2010 was also studied using RS data, which helped in flood damage assessment and it`s effect on final yield. • Both RS, detailed ground information and expert knowledge are needed to get information on varietal discrimination and production. Accuracy of production data was compared with market arrivals in different states by AIREA and the user organization. • The area under Evolved Basmati is increasing due to higher yield and Traditional varieties decreasing in recent years. SALAMAT Thank You