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ICAR AICE-JRF/SRF Crop Area & Yield Estimation: Crop-Cutting Experiments: practice questions

ICAR AICE-JRF/SRF - Agricultural Statistics, Computer Application, Bioinformatics (PhD admission and fellowship) · India · National Testing Agency for ICAR

Original practice questions written to the published syllabus · not past papers
10worked here, free
70written to this exam in this subject
12syllabus units

What this subject covers

How crop production is measured: area from complete enumeration, sample surveys and area frames, yield from crop-cutting experiments on randomly located plots, the Mahalanobis and Sukhatme debate on plot size, crop insurance CCEs, and newer satellite, model and smartphone-based methods.

In the exams: AICE-JRF Ag Stat Unit 5 'crop estimation surveys' (heavy); UPSC ISS Statistics III-IV official statistics (present); state DES statistical-officer exams (present)

What ICAR AICE-JRF/SRF asks from it

ICAR AICE-JRF/SRF Agricultural Statistics (Unit 5)

Crop estimation surveys and the agricultural statistics system: the multistage design of crop-cutting experiments, area statistics, land-use statistics, and estimation of yield and its standard error.

Where the 70 questions fall

How our questions for ICAR AICE-JRF/SRF spread across the units of Crop Area & Yield Estimation: Crop-Cutting Experiments. Difficulty: 15 easy, 34 medium, 21 hard.

Crop-cutting experiments: multistage design of villages, fields and plots8
Field protocol: plot location, harvesting, threshing and driage8
Plot size, shape and border bias in crop-cutting7
Crop-cutting for crop insurance: number of CCEs and technology-assisted CCEs7
Estimation of yield and its standard error at district and state level6
Yield estimation for horticultural and plantation crops6
Remote sensing, crop models and smartphone data in yield estimation6
International practice: FAO guidelines and objective yield surveys6
Estimation of crop losses due to pests and diseases5
Area statistics: complete enumeration, sample-based (EARAS, TRS) and area-frame methods4
Improvement of Crop Statistics (ICS) scheme and quality checks4
Production = area x yield: the statistical structure3

10 practice questions, worked

Choose an answer, then open the working. Each explanation says why the right answer is right and why the others are not.

1 · Plot size, shape and border bias in crop-cutting · medium

Why were circular crop-cutting plots largely abandoned?

  1. They cannot be located at random in a field
  2. They are hard to mark accurately in the field
  3. They have the longest boundary for their area
  4. They always fall across field boundaries
Answer and working

B. They are hard to mark accurately in the field

Circles have the least boundary, but marking a circle accurately in a standing crop is difficult, so squares and rectangles with tapes and pegs are used.

Further reading: Sukhatme et al., Sampling Theory of Surveys with Applications; Panse, Estimation of Crop Yields (FAO)

2 · Field protocol: plot location, harvesting, threshing and driage · easy

How is the crop-cutting plot located within a selected field?

  1. By the investigator choosing a patch that looks typical of the field
  2. By the farmer pointing out the best-yielding part of the field
  3. By random coordinates from a fixed corner of the field
  4. By always placing the plot at the exact centre of the field
Answer and working

C. By random coordinates from a fixed corner of the field

Random coordinates within the field's length and breadth avoid the bias of choosing better-looking patches; the centre is a systematic, not random, choice.

Further reading: Sukhatme et al., Sampling Theory of Surveys with Applications; Panse, Estimation of Crop Yields (FAO)

3 · Field protocol: plot location, harvesting, threshing and driage · medium

A selected field's crop has failed completely due to drought. What should the investigator do?

  1. Record the village's average yield instead
  2. Replace it with the nearest healthy field
  3. Record the experiment with zero yield
  4. Drop it and reduce the sample size
Answer and working

C. Record the experiment with zero yield

Replacing or dropping failed fields biases the estimate upward; a failed crop is a real outcome and is recorded as zero.

Further reading: Sukhatme et al., Sampling Theory of Surveys with Applications; Panse, Estimation of Crop Yields (FAO)

4 · Estimation of yield and its standard error at district and state level · medium

What does the percentage standard error of a yield estimate express?

  1. The standard error as a percentage of the estimate
  2. The proportion of crop-cutting experiments that were lost
  3. The share of yield variance that is due to villages
  4. The difference between this year's and last year's yield
Answer and working

A. The standard error as a percentage of the estimate

Percentage SE = SE / estimate x 100, the CV of the estimate, the usual reliability measure.

Further reading: Sukhatme et al., Sampling Theory of Surveys with Applications; Panse, Estimation of Crop Yields (FAO)

5 · Crop-cutting for crop insurance: number of CCEs and technology-assisted CCEs · hard

Within a village the CV of plot yields is 30%. What is the CV of the mean of 4 crop-cutting experiments (independent plots)?

  1. 60.0%
  2. 30.0%
  3. 7.5%
  4. 15.0%
Answer and working

D. 15.0%

CV of the mean = CV / sqrt(n) = 30/2 = 15.0%. Dividing by n gives 7.5%. Such large errors explain why neighbouring units with equal true yields can receive different payouts.

Further reading: Sukhatme et al., Sampling Theory of Surveys with Applications; Panse, Estimation of Crop Yields (FAO)

6 · Crop-cutting for crop insurance: number of CCEs and technology-assisted CCEs · hard

Statistically, technology-based yield estimates combined with fewer CCEs are an application of which methods?

  1. Regression and small-area estimation with auxiliary data
  2. Latin square designs with villages as rows and fields as columns
  3. Index numbers combining yields of different crops
  4. Systematic sampling with a random start in each village
Answer and working

A. Regression and small-area estimation with auxiliary data

Satellite, weather and model outputs act as auxiliary variables known everywhere, combined with CCE measurements on a sample through regression or small-area models.

Further reading: Sukhatme et al., Sampling Theory of Surveys with Applications; Panse, Estimation of Crop Yields (FAO)

7 · Remote sensing, crop models and smartphone data in yield estimation · medium

How should crop simulation model outputs be used in statistical yield estimation?

  1. As auxiliary information with error, calibrated to local data
  2. As exact observations that replace crop-cutting entirely
  3. As a check on driage factors in drying experiments
  4. Only for crops that have never been crop-cut anywhere
Answer and working

A. As auxiliary information with error, calibrated to local data

Models need local calibration and their predictions carry error; they serve as auxiliary variables in regression or small-area estimators.

Further reading: Sukhatme et al., Sampling Theory of Surveys with Applications; Panse, Estimation of Crop Yields (FAO)

8 · Estimation of crop losses due to pests and diseases · medium

How do survey-based loss estimates usually work over large areas?

  1. Incidence on sampled fields is regressed against yield
  2. Losses are assumed equal to the pesticide quantity sold
  3. Losses are measured only in research stations and scaled up
  4. Every field is protected and unprotected in alternate years
Answer and working

A. Incidence on sampled fields is regressed against yield

Incidence surveys plus damage-yield relationships estimated from experiments allow losses to be estimated over many fields.

Further reading: Sukhatme et al., Sampling Theory of Surveys with Applications; Panse, Estimation of Crop Yields (FAO)

9 · International practice: FAO guidelines and objective yield surveys · hard

Why did methodological studies find farmer-recall yields problematic for research on farm size and productivity?

  1. They always match full-plot harvest exactly on large farms
  2. Bias varying with plot size creates false size effects
  3. They cannot be collected for small farms under any design
  4. They are unbiased but too costly to collect in large surveys
Answer and working

B. Bias varying with plot size creates false size effects

If reporting error depends on plot size, the apparent inverse farm-size productivity relation may partly reflect measurement error.

Further reading: Sukhatme et al., Sampling Theory of Surveys with Applications; Panse, Estimation of Crop Yields (FAO)

10 · International practice: FAO guidelines and objective yield surveys · hard

Which feature is common to well-functioning crop estimation systems across countries?

  1. Selection of fields by local officials who know the best farms
  2. Random selection of fields and plots by the statistics office
  3. Reliance on market arrivals instead of any field measurement
  4. Publication of estimates without methods or standard errors
Answer and working

B. Random selection of fields and plots by the statistics office

Random selection by the statistical office, objective measurement, supervision and published methods and standard errors characterise good systems.

Further reading: Sukhatme et al., Sampling Theory of Surveys with Applications; Panse, Estimation of Crop Yields (FAO)

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Crop Area & Yield Estimation: Crop-Cutting ExperimentsThe full study manual, free ICAR AICE-JRF/SRFFormat, sections and how it has changed

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