Tuesday, 7 May 2013

Crop Yield Estimation

Crop Yield Estimation
The sampling design generally adopted for the crop estimation surveys is one of the stratified multi stage random sampling with thesis as a strata and village within stratum as first stage unit of sampling. Field at each selected villages is sampling unit at second stage and experimental plot of a specified size and shape is the ultimate unit of the sampling.

An estimate can be made at any time. The closer to the harvest date, the more accurate the prediction. The advance estimates which are released in the month of September are related to Kharif crops, which are based on the field observations.

The second forecast, which covers both Kharif and Rabi and released in January by taking into account additional information obtained from various sources including agriculture inputs, incidence of pest and disease, various government reports, conditions of standing crops etc.

The third forecast, which is made in the month of March, the estimates of Kharif and Rabi are revised based on the information received from Market Intelligence sources, Weather Reports etc.

The final comparative report is provided based on the actual figures received from the government/authorized sources.   

Sampling Technique

In making an estimate, the objective is to obtain a 'representative' sample of the block. It is important to select samples randomly (without any human bias) throughout the block. The number of samples required will depend firstly, on the variability within the block. The greater the variability, the higher the number of samples required.
The other factor determining the number of samples is the degree of accuracy required. The more samples taken, the higher the accuracy will be of the estimate. However, there comes a point where the additional time invested will only increase accuracy marginally.

Statistical techniques to calculate the minimum number of samples needed are based on block variability, and provide an estimate within a certain level of confidence (Dunn and Martin 1998). This formula is used:
n = tx CV/ PE2
  • n is the number of samples needed
  • t is the statistical confidence level (t=1: 70% confidence; t=2: 95% confidence; t=3: 99% confidence)
  • CV is the coefficient of variation of the block (= ratio of the standard deviation to the average expressed as a percentage)
    • To calculate CV, take the standard deviation, divide it by the average, and times by 100. Excel can be used to calculate the average (AVG function) and standard deviation (STDEV function).
    • For example, where the CV is 30%, at a confidence level of 95% (t=2) to determine an average within a 10% error, you need to collect 36 samples per block.
    • An initial, random sample of 10 to 20 vines can be used to determine the variability CV. The time spent on any estimate (and thereby the cost) should always match the degree of accuracy required for the estimate.
PE is the acceptable percent of error either side of the average

GIS in Agriculture

žGeographical Information Systems (GIS)
Storage and retrieval of data
Running any kind of query
Statistics generation at different Administrative Boundary level
Diagnostic analysis of system performance
Application Areas
Management and Planning

For more information education or Business queries contact me at arun.balla@gmail.com

Agricultural Information System - Future Requirements

žCreation of cadastral level information system
Field information
Site suitability studies
Soil, salinity, pH
Canal, distributaries network, lakes, ponds
Historical rainfall
General landuse-landcover
žSystem for on-line monitoring of crops
Satellite remote sensing
Crop acreage
Crop health
Yield & production
NDVI values
Climatic parameters
Total precipitable water data
Agricultural practices
Additional info: fertilizers, seeds, etc

For more information education or Business queries contact me at arun.balla@gmail.com

Agriculture Information Systems

žMarket related database
Creation of field-level crop inventory
Location of different Grain Market
Market wise info on major agricultural commodities
Timely forecast of production
Demand and supply of commodities
Transportation network
Shortest route from crop field to Grain Market
Information on current price

Remote Sensing Agriculture - Complete Flow Chart

Acreage Estimation

Crop Health Monitoring

For more information education or Business queries contact me at arun.balla@gmail.com

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