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 papersForecasting harvests before they happen: Fisher's regression integral and weather-indices models, pre-harvest forecasts from biometric characters and farmers' appraisal, agro-meteorological and remote-sensing models, non-linear growth curves (logistic, Gompertz, Richards), and how statistical and crop-simulation models are combined.
In the exams: AICE-JRF Agricultural Meteorology 'statistical models' (present); ICAR MSc/PhD core in modelling; national crop-forecasting programmes (applied); light in national exams
ICAR AICE-JRF/SRF Agricultural Statistics and Agricultural Meteorology
Statistical models for crop-yield forecasting: weather-indices models, Fisher's regression integral, models using biometric characters and farmers' appraisal, crop-weather relations, growing degree-days and remote-sensing indices such as NDVI; growth models and their fitting.
How our questions for ICAR AICE-JRF/SRF spread across the units of Crop Forecasting, Weather-Based Models & Non-linear Growth Models. Difficulty: 28 easy, 52 medium, 18 hard.
| Non-linear growth models: logistic, Gompertz, Monomolecular and Richards | 10 | |
| Crop simulation models and their statistical calibration | 10 | |
| Pre-harvest forecasting: purpose and history | 9 | |
| Weather-based models: Fisher's regression integral and weather indices | 9 | |
| Remote-sensing-based yield forecasting with vegetation indices | 9 | |
| Fitting non-linear models: iterative estimation and goodness of fit | 9 | |
| Machine-learning and hybrid forecasting models | 9 | |
| Evaluation of forecasts and their use in policy | 9 | |
| Models using plant biometric characters | 8 | |
| Farmers' eye estimates and appraisal-based forecasts | 8 | |
| Agro-meteorological and composite models | 8 |
Choose an answer, then open the working. Each explanation says why the right answer is right and why the others are not.
In an objective yield survey of the kind run by the US national agricultural statistics service, what is collected?
B. Plant counts and measurements in sample plots
Objective yield surveys lay out sample plots in fields and count and measure plants and fruit (ears, pods, bolls) to model yield, complementing farmer-reported surveys. Prices and elevator sales are not yield measurements.
Further reading: ICAR-IASRI crop forecasting reports; Fisher (1924)
In double sampling, farmers' appraisals average 24.0 q/ha in a large first-phase sample. In a sub-sample, appraisals average 22.0 q/ha and crop-cutting yields average 25.0 q/ha, with a regression slope of yield on appraisal of 0.9. What is the regression estimate of mean yield?
C. 26.8 q/ha
y-reg = y-bar(sub) + b(x-bar(large) - x-bar(sub)) = 25.0 + 0.9 x 2.0 = 26.8. 25.0 ignores the first phase; 27.3 is the ratio estimator; 27.0 assumes a slope of 1.
Further reading: ICAR-IASRI crop forecasting reports; Cochran, Sampling Techniques (double sampling)
Relief payments in a district depend on reported crop losses. What problem does this create for appraisal-based forecasts?
B. Appraisals may be biased downwards by the incentive
When reported losses bring payments, farmers and officials may understate yields; the bias can change with policy, so calibration must be re-estimated each season.
Further reading: ICAR-IASRI crop forecasting reports; Cochran, Sampling Techniques (double sampling)
Weekly crop-condition ratings report the share of a crop in classes from very poor to excellent. How are they turned into a yield forecast?
D. By regressing past final yields on past condition shares
Condition ratings are subjective; their link to yield is estimated from past years by regressing final yields on the shares or an index built from them, the same calibration logic as for appraisals.
Further reading: ICAR-IASRI crop forecasting reports; Cochran, Sampling Techniques (double sampling)
What is the standardised precipitation index (SPI) used for in crop monitoring?
A. Expressing rainfall over a period as departures from normal in standard units, to identify drought
SPI transforms accumulated rainfall into a standard normal deviate relative to its long-term distribution, so dry and wet spells can be compared across places and timescales.
Further reading: FAO agro-meteorological crop monitoring methods
Which sensor type is preferred for mapping monsoon (kharif) rice when optical images are cloud-covered?
C. Synthetic aperture radar
SAR is an active microwave sensor that sees through cloud; flooded rice fields at transplanting have a distinctive backscatter, which makes SAR the standard tool for kharif rice.
Further reading: Jones, Plants and Microclimate; remote-sensing crop monitoring literature
Which crop simulation model was developed by FAO with a focus on yield response to water?
A. AquaCrop
AquaCrop is FAO's water-driven crop model. CROPGRO is the DSSAT legume model, WOFOST is used in the EU's crop monitoring, and InfoCrop was developed in India.
Further reading: Wallach, Makowski, Jones and Brun, Working with Dynamic Crop Models
A random forest forecasts district yields with very high accuracy under random 10-fold cross-validation, but performs poorly in the next season. What is the most likely reason?
D. Folds leaked correlated years and districts
Yields are correlated in time and space; random folds put the same year or nearby districts in both training and test sets, inflating accuracy. Leave-one-year-out or forward validation is honest.
Further reading: Zhang (2003); Wallach et al., Working with Dynamic Crop Models
Over ten years, a ministry's first advance estimate of rice production has been revised downwards in nine years. What does this suggest?
C. Early estimates have a systematic upward bias
Revisions consistently in one direction indicate bias in early estimates; users will learn to discount them, and the method needs correction.
Further reading: Hyndman and Athanasopoulos, Forecasting: Principles and Practice; ICAR-IASRI reports
A government is deciding whether to permit wheat exports using a forecast. Why should the forecast be issued with an uncertainty range?
D. The costs of over- and under-estimating differ, and decision-makers must weigh the risk of each
Overestimating may lead to exports followed by costly imports; underestimating may trigger unnecessary imports that depress farm prices. Decisions under asymmetric costs need the distribution, not just a point.
Further reading: Hyndman and Athanasopoulos, Forecasting: Principles and Practice; ICAR-IASRI reports
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