Application of Crop Analytics for Product Development and Business Enhancement - Suggestions presented for implementation by Indian Government and Industry specifically the food and agriculture sectors

2 of 24Crop Analytics – India Sridhar Rudravarapu - Dec 2018
Crop Analytics*
“Crop”: A cultivated plant that is grown on a large scale commercially,
especially a cereal, fruit, or vegetable.
“Crop Analytics”
(*Coined by M/s Monsanto Company)
“is the Collection of Compositional Data
Progressing from Proximate Values to Standard and Premium Information”
Crop analytics is a modern technology platform
involving the application of advanced analytical methods and technologies
to identify the composition of food and feed traits
and prediction of its performance for agribusiness operations

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Application of Crop Analytics
 Crop Analytics provides the vital proximate compositional data (Moisture,
oil, protein, ash, fiber and carbohydrate constituents) to define the
nutritional value and product quality for many foodstuffs and agriculture
products.
(Proximate composition also significantly effects/affects the texture,
perceived quality and flavor of products).
 New products with special characteristics are discovered by quick and
precise analysis of grain contents and they can be timely introduced in the
competitive market place.
(Analysis of crops prior to harvest enables both farmers and industry to realize
better returns on their investment. Farmers can achieve higher profits and
industrial buyers can lower the risk of purchasing out-of-spec product by
better analysis and grading of crop quality)
 Crop Analytics supports in providing highest quality traits the customers
expect which is extremely important in international commerce.
Crop Analytics – India Sridhar Rudravarapu - Dec 2018

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Advanced Crop Analytics
Application of Crop Analytics along with
Modern Breeding Techniques and Technology Tools
Useful for
 Trait development and monitoring during crop growth, after harvest and
storage; Improvements in yield, quality, consistency & ease of
management on farm
(Yield prediction for e.g., oil/sugar/ethanol content analysis done by
industry is helpful to assess the raw material availability for export/import
preparations)
 Providing analysis data for regulatory submissions
 Establishing the geographical importance of crop varieties
Benefits of crop analytics employing modern technology include
 Rapid measurements resulting in time saving
 Non-destructive analysis
 Implementable in the field or in the lab
 Simultaneous measurement of multiple constituents
 Cost effectiveness

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Application of Crop Analytics - During Crop Growth
 Useful for estimating yield gap/monitoring photosynthesis
 Imaging & high resolution NMR /NIR spectroscopy support for example in
 Analysis of key plant functions such as distribution of water, carbon
and nutrients.
 Quality assessment and conversion efficiency
(Dry matter can change by 1-2% per day once the crop has reached 45% dry
matter. Varying moisture for example in the corn crop could make kernel drying
difficult ).
 Breeder selection activities
[Accelerated variety selection for valuable and inheritable genetic traits for
example, cyst-nematode (Heterodera schachtii) resistant traits in sugar beets].
 Spectral imaging
(Drone camera supported by algorithmic expertise can translate spectral
imaging data in to valuable information allowing farmers to substantially
improve yield efficiency and reduce their usage of fertilizers and chemicals. Ref:
GAMAYA; https://gamaya.com)

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Application of Crop Analytics - After Crop Harvest
 Useful to the Industry for incoming raw-material identification and Quality
Control/Assurance
Following applications indicate few examples for the analysis of Crop/Fruit/
Vegetable elemental parameters using VIS/NIR spectra (400–1700 nm)
 Moisture, oil, oleic and linoleic acid contents in intact Olive
 Sugar, soluble solids, lycopene, 𝛽-carotene, and carotenoids in Tomato
 Sensory aligned texture of cooked Potato
 Dry Matter/Nutritive Value of fermented whole crop (Cereal Silage)
[Harvesting should take place only when it has reached the soft-cheddar
consistency (i.e. above 35% DM) and until the cereal grain has reached the hard-
cheddar consistency (approx. 55% DM)]
 Levels of vitamin C, total polyphenol, sugars--sucrose, fructose, sorbitol
& glucose in fruits and vegetables
 Sugar concentration for its sensory evaluation & storage ability in Melon
 FT-IR Spectra of Sugars in Apple Varieties

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Application of Crop Analytics - During Crop Storage
 Quality and price of the grain is decided by its moisture content
at the stage of harvesting, storage, processing and marketing.
 Stored pockets of grain material with excess moisture content during
long term storage is prone to insect/fungus infestation causing elevated
levels of CO2
 CO2 sensors are useful to detect and monitor CO2 levels in the
headspace of a storage structure to provide an early warning about
grain spoilage.
for example, relative CO2 concentrations indicate
Safe at 400-500 ppm
Problem at 1,000 ppm
Spoilage at 3,000 ppm
Carbon dioxide Sensor
(GS-CO2)

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Evolution of Crop Analytics
Analytical Methods & Technologies Employed for Crop Analytics
 Crop Analytics has progressed from traditional methods of
sensory evaluation, chemical and microbiological analysis to
sophisticated modern spectral techniques.
Wet Chemistry:
 The proximate system for routine analysis devised first in Germany
(Henneberg & Stohmann, 1860-64) provided a top level, very broad,
classification of food components as water (moisture), ash, crude fat
(ether extract), crude protein and crude fibre.
 Nitrogen-free extract more or less representing sugars and starches was
calculated by difference rather than measured by analysis.
 Measurement of ‘total fat’ by this way also has limited value for
nutritional purposes as it is influenced by the methodology adopted.
(The proximate analysis including the original methods, still forms the basis for
the analysis of food and feed for labeling and legislative purposes in many
countries) !

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Evolution of Crop Analytics
Analytical Methods & Technologies Employed for Crop Analytics
 Cost Effective Solutions Adopted for Grain and Seed Analysis
 Changes of moisture content in the grain affects the dielectric constant and
the resultant capacitance variation in turn is converted to voltage variation
and calibrated in terms of moisture%.
 Microprocessor based measurement systems have been in operation to
test the grain moisture in the range of 5 to 25% with an accuracy of +/− 0.5%.
Several crops for example Wheat, Paddy, Soybean, Sunflower and Mustard
have been analyzed for measurement of moisture% using rigorously
calibrated systems which can be applied for other grains also.
 Change/Modification of Definitions
For example, the AOAC INTERNATIONAL has adopted methods using acid
hydrolysis and capillary gas chromatography to determine the total fat in
foods (complying with the definition of fat as the sum of fatty acids
expressed as triacylglycerols).

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Evolution of Crop Analytics - Advanced Approaches
 CROP ANALYSIS TIME HAS CHANGED FROM DAYS TO MINUTES !!
(by applying the optical techniques especially the multispectral methods
in visual and near-infrared wavelengths)
Near-Infrared Spectroscopy (NIR)
 NIR is a rapid, secondary method based on the mathematical
relationship (regression) with the accepted wet chemistry method.
Sophisticated software packages are used to perform the
mathematical calculations and relations (“prediction models” or
“calibrations”) necessary to associate the NIR-produced spectra of
specific reference samples with the actual wet chemistry results of
those samples.
 A robust NIR calibration is developed and maintained with crop
samples from diverse genetic and environmental backgrounds
including the reference samples analyzed by traditional wet chemistry
methods.
 Sample preparation and presentation to the NIR instrument varies
widely. Usually dried and finely ground samples are employed but
fresh whole grains or un-ground samples can also be scanned.

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NIR – Advantages & Limitations
 NIR provides significant advantages during the manufacturing stage with
its ability to analyze large numbers of samples simultaneously in real-
time, continuously and non-destructively.
(This feature provides cost advantage to the producers compared to the
more expensive wet chemistry. This also helps producers better manage
feedstuff nutrient variation by more frequent analysis).
 Tailored applications addressing unique product requirements can be
made with customizable libraries and methods .
 As NIR can scan only the surface, it is difficult to scan the opaque surfaced
samples and also complex for calibration purpose. NIR also has limited
applicability for the quality control of oil (fat) content in many foods.
 NIR and FT-NIR (Fourier transform NIR) instruments are comparable in
predictive performance and their scan time. Though sample preparation
and presentation method is easier in FT-NIR, it requires more sample size
compared to the NIR.

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NIR Spectrometer
Cited below are examples of some models which have been applied for
rapid on-site material identification and analysis to meet high product
quality demands through increased testing for grain, flour and
agricultural markets.
• Thermo Scientific microPHAZIR GP analyzer (Handheld instrument)
• Unity Scientific InfraStar RTW (1180 – 2180nm and 1400 - 2400nm)
• Brukers Optic’s Matrix-I FT (835 to 2502 nm)
• Foss-NIR reflectance -6500 (450 to 2498 nm)

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NIR Application in Agriculture
 Phil Williams and Karl Norris were the first ever to apply NIR technology
for large-scale, real-time testing of commercial commodities.
 NIR is advantageous over traditional laboratory relegated instruments
and techniques because of proven and versatile solutions for the
qualitative and quantitative agricultural analysis necessary throughout
the entire production process, including the process monitoring.
 For example, NIR technology customized by the Neotec Corporation
resulted in replacement of the traditional Kjeldahl method by an
automated NIR system making it possible to test 600,000 samples per
year for a wheat protein segregation program several years ago.
 NIR technology also enabled Pioneer company to characterize the
energy value of a hybrid for poultry or swine feed purpose in 21 minutes
compared to the earlier analysis time of 21 days in the yester years !
(Pioneer grain analytics lab analyzed multiple thousands of grain samples procured
across the country and sampling in multiple locations).

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NIR Applications for Food Crops
 Analysis of
 Fruits and Vegetables
 Forages and Feedstuff
 Small Grain Crops
 Oilseeds
 Coarse Grains
 Sugarcane
 Coffee, Tea
 Spices
 Medicinal Plants and Aromatic Plants . . . . .
NIR applications facilitate rapid and accurate in-field identification and
analysis of many constituents including lutein, cellulose, hemicellulose,
lignin, ash, glucan, xylan, carbohydrates and chlorophyll …..

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NIR Applications for Food Crop Related Products
 NIR applications for example can be helpful for
 Maize testing for the animal feed (poultry) and industrial starch
 Analyzing Maize hardness to classify the flours according to their food
uses suitability (The particle size index is a good indicator of the
hardness of the grain and is closely correlated with other quality
parameters of flour. Similar analysis can be done for other potential
crops such as Oats, Small Millets etc.)
 Production of spirits/industrial alcohol & starch in Sorghum & Millets
 Testing gluten content for proper storage & processing of Wheat grain
 Quality verification of milling by-products and to optimize the
production of low cost formulations while monitoring and controlling
the tempering and milling process
 Backup support with regard to finished product label claims

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Nuclear Magnetic Resonance (NMR)
 Breeders, growers and buyers rely on accurate and fast determination of oil
content for realizing the commercial value of oil crops such as rape (canola),
sunflower, linseed, soybean and groundnut……
 Solvent extraction techniques commonly used for the determination of fat
content are slow, cumbersome, often employing hazardous chemicals and
requiring highly skilled personnel.
A bench top NMR instrument
 Provides a fast, direct, user-friendly method to determine the fat/oil
content in foodstuffs. In contrast to the standard wet chemistry
methods, it can be operated by a non-NMR expert.
 Can be used with a simple calibration employing a single reference oil
sample to measure oil content from from zer0 to 100% .
 Offers a very stable, long term solution with a minimal recalibration,
sample preparation and short measurement time (ca. 20 sec) .
 Technique is not sensitive to sample matrix granularity and additives
such as spices, flavors, colors, and salt.
 A nondestructive solution facilitating easily repeatable measurements.

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Crop Analytics Support for
Demand Driven Agriculture & Commercialization
It has been said and known
“Crop Yield is not solely tonnage of biomass produced in the field, rather it
is the proportion of the crop that can be harvested and brought to market
in a condition and at a price acceptable to the consumer”
(HortScience February 2009 vol. 44 no. 1 20-22)
“You can exploit differences in hybrids if you know how to measure those
differences” (Farm Industry News; Dec 01, 2003)
“Not all corn is the same. As grain analytical tools improve, expect to see
more new products with specific characteristics”
“An agricultural produce brought to the market differs much from lot to
lot due to varietal differences, management practices followed and varied
agro-climatic conditions. It is essential to grade the produce on scientific
grounds assuring quality to buyer and premium price to producer-seller”
 Crop Analytics addresses the varying remunerative prices in the market
by maintaining the quality standards.

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 Market for premium food crops/products is increasing
 Crop analytics is key for the analysis of grain composition and
prediction of its performance for grain processors or feeders.
 Public-Private initiatives gradually replaced feed Barley with high
yielding, good malting-type Barley varieties.
 Crop Analytics supports the production and quality standards of high
value crops being grown more for example Corn, Guar, fruits……
Guar crop grown for cattle feed requirements of
milking cows and buffalos in Rajasthan and Haryana
states in India has become a hot item due to the usage
of its gum extract as an additive in food products and
in deep sea oil well drilling purposes (viscosity
enhancer) and thus Guar crop becoming a good foreign
exchange earner for India !

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 Geographical varieties that have very good taste and nourishment value
are in great demand in retail markets of India and abroad
 For example some forgotten Desi Rice varieties [‘Champakali’,
‘Govind Bhog’, ‘Ambemohar’, ‘Ghansal’, and ‘Jirga’] have attained
premium food status and are sold for twice the price of the
commonly consumed rice varieties in India.
 Durum Wheat varieties (‘Malwa’ and ‘Sharbati’) of India are also
sold at a price premium.
 Wheat varieties with higher protein value and suitable for flour blending
are being sought by the rapidly growing baking/ confectionary industry.
India needs to grow/develop wheat varieties yielding specialty
flours (e.g. for use in pizzas and burger buns) and to cater to the
rapidly growing modern fast food industry.

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Application of Crop Analytics – Global Initiatives
Industrial Majors
 Monsanto: Its crop analytics research program employing NIR analysis
has supported the discovery of corn (HFC) hybrids more fermentable
in the dry grind ethanol process and yielding the most ethanol
(Processor Preferred Label).
Monsanto’s Mobile Lab (CAML) fitted with a fast Gas Chromatography
(GC) system as well as NIR provided a full fatty acid profile on grain/oil
in less than 10 minutes. This ensured that its low-linolenic Vistive
soybeans were not contaminated with commodity soybeans and the
trans fats were reduced/eliminated in the processed foods.
 Monsanto & Perten Instruments: Applied advanced technologies for
monitoring quality traits such as nutritional, technological ability/
texture and aroma characteristics (highly valued for baking and other
food uses not found in the commercial hybrid varieties).
 ASD Technology: Used NIR solutions in a variety of vegetation analyses
(measurements of protein, moisture, fats, amino acids, and other
constituents) in corn, canola and soy bean seeds.

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Crop Product Pricing Scenario in India
 In the traditional agricultural production and marketing, high quality
crop product is usually mixed with lower quality products
(In general producers do not receive higher prices for producing high quality
or special product characteristics in India)
 The procurement of raw material is done at the same price
irrespective of the differences in the crop product from each
producer with regard to extractability, actual content differences
and other ingredient influences etc.
 For example, price for the pulse crops in India is fixed by the traders
& commission agents by eye sight method !!!
(Considerable deviation exists between eye-sight grade and scientific grade).
 The Indian Commission for Agricultural Costs and Prices while
recommending minimum support prices takes into account factors
such as cost of production, change in prices of inputs, demand and
supply, market price trends and cost of living among other factors.
Strangely the quality strengths are not taken in to account!!
Organized Crop Analytics is the key for effective commercialization
of crop produce in domestic and international markets.

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Key Suggestions for Application of Crop Analytics in India
Implementation of Organized Analytics by the Government
 Develop infrastructure at various levels and focus on selection of
appropriate analytical tools/technology for high throughput
analysis with fast turnaround for the scientific and industrial
activities and facilitating quick decision making.
 Align Chemometrics tools with Crop Analytics and implement
common reference/International standards .
 Perform facility accreditation assuring the end-users and producers
that the products are providing the highest quality traits as per
customers’ expectations (This will enhance the producers’ ability to
remain competitive in the international markets).
 Facilitate satisfactory grading system and grading laboratory
equipped with suitable equipment for quick and correct estimation
of quality characteristics (This will assure right price fixation of the
crop produce).
 Ensure that agricultural products intended for widespread food
consumption conform to international quality standards right from
the field to the end processing (Thus assuring profitable prices).

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 Value Identification (Breeder)
Find commercially useful traits (Established/Potential)
An example in this regard is the improved Samba Masuri (ISM) variety rice
with Low Glycemic Index developed by the Centre for Cellular and Molecular
Biology in association with Indian Institute of Rice Research.
 Value Realization (Farmer)
 Survey all Indian public varieties for their analyzed trait contents
and realization in the market.
 Value Addition/Maintenance (Marketer/Industry)
 Consider quality strength of the produce while recommending
minimum support prices (by the Indian Commission for Agricultural
Costs and Prices)
 Promote seed positioning and encourage the crop producers with
premium traits
 Maintain a common database and control system for Crop
Analytics activities.

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 Focus on Investments for Application of Crop Analytics (along with
specific tools/technology) in the 5-Year Plan Preparations
(One guiding example to cite is the Dutch Strategic Investment Plan 2012-2021
focusing on specific investment in NMR Spectroscopy to address challenges in
areas such as health & life science and sustainability).
 Establish New Crop Analytics Research Centers - Crop Wise at
Centralized Locations with all the Necessary Equipment Catering to
Diverse Testing Needs
 Better Organize the Existing Analytical Resources
(Stop duplication of instrumentation purchase, analysis and standardizations
for common material. Address any scenario where instruments are available
but qualified skilled technicians or money for standardization are not available.
Also address under utilization or inadequacy of equipment).
 Preserve Landraces in the National Germplasm Bank and define
Quality Clusters based on Characterization with respect to
Technological (e.g. flour viscosity, hardness) and Nutritional (e.g.
protein, oil, fiber, carotenoids) Quality Traits
*******

Application of Crop Analytics for Product Development and Business Enhancement - Suggestions presented for implementation by Indian Government and Industry specifically the food and agriculture sectors

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Application of Crop Analytics for Product Development and Business Enhancement - Suggestions presented for implementation by Indian Government and Industry specifically the food and agriculture sectors