Sampling Spatial Units for Agricultural Surveys

Benedetti, Roberto; Piersimoni, Federica; Postiglione, Paolo

doi:10.1007/978-3-662-46008-5

Cited by 40 publications

(31 citation statements)

References 106 publications

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“…The definition of appropriate spatial sampling designs represents an enormous challenge for statisticians and researchers who work with geographically distributed data. The specific characteristics of the geo-referenced populations should be considered when designing a sample Benedetti et al, 2015;Benedetti et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

“…In particular, we used the following R packages: sampling (Tillé & Matei, ), survey (Lumley, ), spsurvey (Kincaid & Olsen, ) and BalancedSampling (Grafström & Lisic, ). For further details about the code that can be employed to estimate spatially balanced samples, see Benedetti et al ().…”

Section: Empirical Evidencementioning

confidence: 99%

“…In many cases, the use of complex sampling schemes implies that weights should be used and that the variances of the survey estimators be computed in a way that reflects the complexity of the sample design. For a comprehensive discussion on this topic, see Chambers et al () and Benedetti et al ().…”

Section: Introductionmentioning

confidence: 99%

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Spatially Balanced Sampling: A Review and A Reappraisal

Benedetti

Piersimoni

Postiglione

2017

Int Statistical Rev

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Summary Spatially distributed data exhibit particular characteristics that should be considered when designing a survey of spatial units. Unfortunately, traditional sampling designs generally do not allow for spatial features, even though it is usually desirable to use information concerning spatial dependence in a sampling design. This paper reviews and compares some recently developed randomised spatial sampling procedures, using simple random sampling without replacement as a benchmark for comparison. The approach taken is design‐based and serves to corroborate intuitive arguments about the need to explicitly integrate spatial dependence into sampling survey theory. Some guidance for choosing an appropriate spatial sampling design is provided, and some empirical evidence for the gains from using these designs with spatial populations is presented, using two datasets as illustrations.

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Section: Discussionmentioning

confidence: 99%

Section: Empirical Evidencementioning

confidence: 99%

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Spatially Balanced Sampling: A Review and A Reappraisal

Benedetti

Piersimoni

Postiglione

2017

Int Statistical Rev

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“…Hidiroglou and Estevao (2013) used a follow-up sample of the non-respondents to deal with nonresponse. Now we apply (2) in Theorem 1 to obtain a design-unbiased estimator of V ar(T 2 ). For convenience, we use (i, j) to denote design stratum j within land-use stratum i.…”

Section: The Alus Estimatormentioning

confidence: 99%

“…In (9), the first two summands give the first quantity in (2); summand 3 and 4 give the second quantity in (2); and the last summand gives the third quantity in (2). V ar(T 2 ) can be further simplified to…”

Section: The Alus Estimatormentioning

confidence: 99%

Adjusting for Misclassification: A Three-Phase Sampling Approach

Sang

Lopiano

Abreu

et al. 2017

Journal of Official Statistics

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(NASS) conducts the June Agricultural Survey (JAS) annually. Substantial misclassification occurs during the prescreening process and from field-estimating farm status for nonresponse and inaccessible records, resulting in a biased estimate of the number of US farms from the JAS. Here, the Annual Land Utilization Survey (ALUS) is proposed as a follow-on survey to the JAS to adjust the estimates of the number of US farms and other important variables. A three-phase survey design-based estimator is developed for the JAS-ALUS with nonresponse adjustment for the second phase (ALUS). A design-unbiased estimator of the variance is provided in explicit form.

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A spatially balanced design with probability function proportional to the within sample distance

Benedetti

Piersimoni

2017

Biometrical J

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The units observed in a biological, agricultural, and environmental survey are often randomly selected from a finite population whose main feature is to be geo-referenced thus its spatial distribution should be used as essential information in designing the sample. In particular our interest is focused on probability samples that are well spread over the population in every dimension which in recent literature are defined as spatially balanced samples. To approach the problem we used the within sample distance as the summary index of the spatial distribution of a random selection criterion. Moreover numerical comparisons are made between the relative efficiency, measured with respect to the simple random sampling, of the suggested design and some other classical solutions as the Generalized Random Tessellation Stratified (GRTS) design used by the US Environmental Protection Agency (EPA) and other balanced or spatially balanced selection procedures as the Spatially Correlated Poisson Sampling (SCPS), the balanced sampling (CUBE), and the Local Pivotal method (LPM). These experiments on real and simulated data show that the design based on the within sample distance selects samples with a better spatial balance thus gives estimates with a lower sampling error than those obtained by using the other methods. The suggested method is very flexible to the introduction of stratification and coordination of samples and, even if in its nature it is computationally intensive, it is shown to be a suitable solution even when dealing with high sampling rates and large population frames where the main problem arises from the size of the distance matrix.

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Sampling Spatial Units for Agricultural Surveys

Cited by 40 publications

References 106 publications

Spatially Balanced Sampling: A Review and A Reappraisal

Spatially Balanced Sampling: A Review and A Reappraisal

Adjusting for Misclassification: A Three-Phase Sampling Approach

A spatially balanced design with probability function proportional to the within sample distance

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