Application of GIS and Big Data Analytics in Agribusiness

Abstract:                                                                                                                                           “The world doesn’t need any more engineers. We didn’t run out of planes and television sets…we ran out of food.”- INTERSTELLAR (Movie)                                                                 Risk and uncertainty are ubiquitous in agriculture. There are numerous sources of risk in Agri-business such as uncertainties in weather, unpredictable and uncertain nature of biological processes, the pronounced seasonality of production and market cycles, the geographical reach, scope and separation of producers and end consumers of agricultural products. Managing agricultural risk is of particular importance considering the subsistence nature of cultivation in India and smallholder farmers, who are usually more vulnerable because of low adaptive capacity.


The objective of this paper is to apply ESRI GIS software package and GIS models to Agribusiness Industry and to identify the need of aggregator such as big data analytic for strategic management. The Agricultural Supply Chain comprises of host of interrelated activities from monitoring soil health, water, and nutrients to managing agricultural waste. The major elements of this ecosystem are ‘On Farm Production’, Soil Health, Water, Nutrients, Pests/Control, Energy, Processing, Inspection, Transportation, Storage Retailers, Inventory, Food Safety, Waste and Smart Services.

The pain area where ESRI GIS solution, Remote Sensing and Big Data Analytics can help is by;

  • Predicting Crop yield for all crop types (Rabi, Khareef) Block wise, District-wise and State-wise.
  • Identify clusters (land areas heterogeneous within) for cluster analysis of input data like weather data, soil types, farmers’ input using hand held devices
  • Integration of all input services for live update of big data repository
  • Usage of remote sensing imageries to track changes on the surface

This paper focuses on the role of ESRI GIS solutions, Remote Sensing and Big data analytics as strategic IT enablers in Agribusiness.

P.S. If interested please email to for a complete copy of this technical paper. #Agilytics

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Business in my blood

The aim is crystal clear. The years of working for others and toiling to earn experience and problem solving skills, have finally led to believe that ‘Business is in my blood’. The most basic element of creating and running a business is in the accurate Financial Management.
To be wise, money-wise, is not a skill which is only learnt in a business school. This skill is seeded by the parents when you proudly break your money-bank (gullak) to be guided to invest in fulfilling your dream. The skill to save and invest originates from the childhood and built upon throughout your professional journey.
Having said that the challenge of creating a new business involve crucial initial period of seeding. But these challenges and the desire to face them, will ultimately set the company to the journey of success.
I love my aim. I can’t sit idle. I am on it.

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Start-up Analytics

To be an entrepreneur is like stepping onto a roller-coaster ride in the world of business. There are ups, there are downs and there are horrifying turns. Sometimes you feel a little frustrated afterwards and sometimes you are inexplicably happy. No matter how many times you ride the coaster or how many people have rode it before you, each go-around feels different. The unpredictability, the uncertainty and the risk are what drive the entrepreneurs to the startup life.

Fortunately, they can find some comfort in start-up analytics. They are logical, they’re rational and they make sense of this challenging lifestyle.But why do so many entrepreneurs find startup analytics intimidating? There are a number of open questions, a lot of blanks and a lot of gray areas. Throw in terms like “growth hacking” and “lean startups” and it’s enough to confuse any beginner. This article is to demystify this world of analytics! It’s not as daunting as it seems.

Metrics Do Matter

First of all the metrics help startups set their goals. And the goals are just dreams with deadlines. Without metrics, it would be impossible to set goals and measure the progress towards them. The goals allow us to be constantly improving and constantly pushing forward.Metrics also help entrepreneurs make smart, informed decisions about the startup. One can identify trends and patterns, problem areas and successes, and potential next steps. Before making major decisions e.g. product iterations and raising capital, startups can consult their metrics. Is it the right time? Will investors take it seriously? Of course, there are a number of other reasons why metrics matter. Progress is the number one factor that motivates us at work. Sharing updates with your team keeps them motivated, informed and focused. Startup launches are losing their PR power, and more and more tech journalists are gravitating towards stories based on growth/success metrics.Without metrics, we don’t know how far we have or have not progressed. We don’t know when our startups are in trouble until it’s too late. We don’t know how to make decisions based on anything other than “entrepreneur intuition”, which definitely doesn’t have a high success rate.Let’s start with a basic fact. Startups are typically at different stages. One startup might be 15 people strong, scaling to 1 million paid users. Another startup might be two co-founders looking for their first 100 free users.The most important metrics depend on the stage of the product. Prior to product/market fit, one should focus on engagement metrics and qualitative feedback from users. This might mean churn, depending on the product category. The better the engagement, the better you set yourself up for growth later on. After the product is working and growing slowly, then the focus would be primarily on growth metrics like signup percents and invite rates.In the beginning, one should focus on engagement metrics and the feedback from users. The reason is that we are still seeking validation and perfecting the product. In the later stages, focus should be on growth metrics. Still, many new entrepreneurs skip right to those growth metrics and many experienced entrepreneurs are stuck on their engagement metrics.What growth metrics should the team be focusing on? Once again, the answer is relative. It really does depend on what your goals are and what you are trying to accomplish in 1, 3, 6, 12 months.Every startup is different, every entrepreneur is different. We all have different goals and different plans for achieving those goals.

Importance of Analytical Context

Entrepreneurs are famous for their willingness to take risks and trust their guts. So, when does context come into play with startup analytics? The simple answer is often.I see people make decisions that are backed by the metrics, but violate common sense. If your ad is working but it is boring, you have a problem. Because as soon as you stop running the ad, the clicks will stop.Sometimes the numbers pull you in one direction when your gut or common sense is pulling you in the opposite direction. Who should you let win? You have to think about analytics in context. People talk about how a landing page may be ugly but acceptable as it converts. Then you need to find how to make it better without ruining the conversion rate–because your image matters too. Startups are bound to operate in the intangible sometimes. The branding and public image is just one example. Measure the tangibles and act on the analytics, but have the intangibles in the back of your mind at all times. Just because you can’t see them on a dashboard doesn’t mean they don’t exist. Sometimes you just have to trust your gut and ignore the numbers in favor of common sense.Always look at your numbers in context.

Startup Analytics Mistakes

1. Dealing in the Success Theater

When one deals with rosy metrics, one actually plays in success theater. You see every aspect of the startup through rose-colored glasses. Startup analytics just don’t work like that. Entrepreneurs can only afford to deal in metrics that help them make decisions about their startups, metrics that lead all the way down to the bottom-line. The problem with rose-colored metrics is that they are the easiest to spot and the easiest to measure.

 2. Focusing only on the Long-term or Short-term goals.

One of the most common questions entrepreneurs have about startup analytics is whether they should focus on the long-term or the short-term. The answer is both.. Let’s say your goal is to increase product signups 30% month over month for three consecutive months. Do you sit in your analytics dashboard for twelve hours every day? The answer is in balancing the short-term with the long-term.

3. Getting Data but Ignoring Action

Collecting data is a great idea for startups at any stage. Hoarding data or neglecting action is not. There is a very big difference between collecting data for the sake of saying you’re collecting data and collecting data to help you make informed decisions and take action. Since the popularity of big data, it seems that some startups believe the more data the better. That’s just not true and it’s not practical for entrepreneurs.

Startup Analytics Best Practices

  • Adopt a lean approach for the Startup Analytics
  • Move from a broader view to narrow views
  • Emphasis on high numbers
  • Utilize testing strategy seriously

When you are at the top of the roller-coaster and waiting for the drop, you want to be sure the coaster is well-built, well-oiled, and well-maintained. That’s where startup analytics come into play. In an up and down world, they help you make informed predictions. Start-up analytics help you ensure you are moving forward. More importantly, they help you avoid the types of drops that startups just don’t recover from.

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Fear of change

“It takes courage to let go of the familiar and embrace the new”.

Recently I talked to one of my close friends who was in trouble for no valid reason. When I dug deep, he
accepted the fact that it may be due to conflict between his push to adopt new learning and the fear of change in the mindset of one senior manager. This guy is leading the software division and happily claims to be a non- technical resource in a technology company.                                                                                                                                                 Quite surprising! Isn’t it.
More surprising was I, when my friend told me that this guy stopped him to impart advanced Python training by quoting directly that “this is not a training institute”. These words were from this guy, as my friend completed beginners level Python training to 30-40 personnel. The feedback was satisfactory.
My friend was pursuing part time MBA from a reputed management institute and his endeavor to change and adopt new technologies like Python, R and Data Sciences had actually worked against his image. This senior manager  was fearful of change. He was unable to accept the initiatives by his junior.
Sadly this may be a common situation in some companies.
It is therefore high time that upper management of companies to wake up  and analyze the mindsets of senior managers.

It’s the high time and in future there will be ‘no time’.

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ESRI ArcGIS Server Java Script API, Geoenrichment

It’s not hard to create a map and overlay one set of data on it – well, at least not with ArcGIS Online. But what if your users really want to see several types of data, such as demographics, just around particular spots on the map, so they can easily hone in on what matters and compare apples to apples from one spot’s data to another? They could be kind of stuck without a developer like you.

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Switch on labels

Here is a small piece of code, which can be used to switch on labels. Why do I feel it to be on the post?  Well, due to its simplicity. Have a look: –

Public Sub SwitchOnLabels(pFeatLayer As IFeatureLayer, DisplayField As String)

Dim pGFLayer As IGeoFeatureLayer
Set pGFLayer = pFeatLayer

Dim pLabelEngine As ILabelEngineLayerProperties
Set pLabelEngine = New LabelEngineLayerProperties
Dim pAnnoLayerProps As IAnnotateLayerProperties

Set pAnnoLayerProps = pLabelEngine
Set pAnnoLayerProps.FeatureLayer = pGFLayer

Dim exp As String
exp = "[" & DisplayField & "]"
pLabelEngine.Expression = exp
pLabelEngine.IsExpressionSimple = True
pGFLayer.AnnotationProperties.Add pLabelEngine
pGFLayer.DisplayAnnotation = True
End Sub

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The following VB 6.0 sub- procedure can be used to dissolve features based upon desired attribute fields: –

Public Sub GPDissolve(strIn, strOut, disField As String)

On Error GoTo EH
Dim GP As IGeoProcessor
Set GP = New GeoProcessor

GP.OverwriteOutput = True

Dim GPU As IGPUtilities
Set GPU = New GPUtilities

'Declare and set a variant array to hold the parameters
Dim parameters As IVariantArray
Set parameters = New VarArray

'Populate the variant array with the parameters
parameters.Add strIn '(in_features)
parameters.Add strOut '(out_feature_class)
parameters.Add disField '(dissolve_field)
parameters.Add "AREA SUM"

'Now execute the Dissolve tool
GP.Execute "Dissolve_management", parameters, Nothing

Exit Sub
'Show all application errors, and severe GeoProcessor messages
MsgBox "A Runtime Error Occured. Error Number: " & CStr(Err.Number) & _
vbCrLf & "Error Description: " & Err.Description & vbCrLf & vbCrLf & "GP Message: " & GP.GetMessages(2), vbExclamation + vbOKOnly
End Sub

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