ASRB NET Crop Area & Yield Estimation: Crop-Cutting Experiments: practice questions

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

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

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

4 · Improvement of Crop Statistics (ICS) scheme and quality checks · medium

Which scrutiny check is most useful for detecting a data-entry error in crop-cutting schedules?

1. Accepting all values, since errors cancel out across the district
2. Rejecting all plot yields that differ from the previous year's yield
3. Checking that every plot yield is exactly equal to the district mean
4. Flagging yields far outside the plausible range for that crop and area

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%

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

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

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

A team has NDVI for every field in a district and CCE yields on a random sample of fields. Which estimator combines them best for mean yield?

1. A regression estimator using NDVI as the auxiliary variable
2. The largest CCE yield multiplied by the mean NDVI
3. The mean NDVI multiplied by a fixed national conversion factor
4. The simple mean of the CCE yields, ignoring NDVI entirely

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

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