1. Using County Assessor's Records To Improve Data Collection Efforts For The June Area Survey Denise A. Abreu, Wendy Barboza, Matt Deaton and Linda J. Young National Agricultural Statistics Service June Area Survey (JAS) The JAS is an Area-frame based survey. The JAS is conducted annually. Because there are not any overlaps or gaps on the Area Frame, it is theoretically complete sampling frame. The JAS uses a 5-year sample rotation scheme, where 20% of the sample is replaced every year (new segments) and 80% of the sample is not replaced (old segments). The JAS has a stratified sample based on land-use and percent of cultivation (JAS strata). Building NASS’s Area Frame and Selecting JAS Sampling Units All sampled segments are screened for potential agriculture in May. The red outlined area represents a JAS sampled segment. Sampled segments are divided into tracts representing unique land operating arrangements, represented by the blue outlined areas with letters. In-person interviewers screen and classify each tract as either agricultural or non-agricultural. Crop and livestock information is collected only on the agricultural tracts when the survey is conducted in June. No survey information is collected on the non-agricultural tracts. Preparing JAS Sampling Units for Screening Agricultural Operations NASS purchased geo-referenced parcel level data from CoreLogic Inc, which was compiled from county assessor tax records and land owner’s information. Each CoreLogic Parcel contained, names and addresses, a property indicator (i.e., duplex, vacant, etc.), and a land use indicator (i.e., school, commercial, vineyard, dairy farm, etc.). CoreLogic parcel data cost a significant amount of money. The data required standardizing the names and addresses in order to prepare reports for field enumerator. Purchased County Assessor’s Data Help During Screening of JAS Agricultural Operations Methods We used Generalized Linear Mixed Models to conduct our analysis. The analysis focused on the total number of agricultural operations in each segment. We assumed a Poisson distribution, and used the default Log link function. The segment sampling weights were re-scaled so that sum of weights k would corresponds to the exact number of units involved in the study. The total number of agricultural operations in each segment was compared between treatment and control. Results and Discussion Effect F ValuePr > F Treatment vs. Control1.260.2611 CDL Group120.96<.0001 Contains FSA data (Y/N)29.50<.0001 New Segment Flag0.460.4981 AG Census Region7.66<.0001 JAS Strata315.90<.0001 Treatment * CDL Group2.820.0377 Treatment * Contains FSA data7.520.0061 Treatment * New Segment Flag0.080.7758 Treatment * AG Census Region4.240.0007 Treatment * JAS Strata2.800.0244 CDL Group * Contains FSA data35.29<.0001 CDL Group *New Segment Flag3.180.0229 CDL Group * AG Census Region28.81<.0001 Contains FSA data *New Segment Flag9.370.0022 Contains FSA data * AG Census Region31.08<.0001 Contains FSA data *JAS Strata52.55<.0001 New Segment Flag * AG Census Region13.14<.0001 EstimateStandard Errort ValuePr > |t| Less than 1% CultivationTreatment vs. Control-0.10070.1698-0.590.5532 1% - 24.9% CultivationTreatment vs. Control-0.18460.1698-1.090.2769 25% - 74.9% CultivationTreatment vs. Control-0.25730.1715-1.500.1336 75% + CultivationTreatment vs. Control-0.21470.1742-1.230.2179 Least Squares Means Analysis EstimateStandard Errort ValuePr > |t| CoreLogic OnlyTreatment vs. Control-0.10070.1698-0.590.5532 CoreLogic & FSA CLUsTreatment vs. Control-0.18460.1698-1.090.2769 EstimateStandard Errort ValuePr > |t| > 50% CultivatedTreatment vs. Control-0.001470.03332-0.040.9648 15% - 50% CultivatedTreatment vs. Control0.012390.032260.380.7009 Less than 15% CultivatedTreatment vs. Control0.090470.041962.160.0311 Ag Urban or CommercialTreatment vs. Control-0.22640.1077-2.100.0356 Non-agriculturalTreatment vs. Control-0.82160.8298-0.990.3221 There was no overall significant effect of the CoreLogic treatment when compared to the control. Even though, there was a significant effect of treatment by CDL groups, FSA data and AG Census region, there was no difference in the least squares mean of the individual groups. However, there was a significant effect of treatment by JAS strata. Fewer agricultural operations were found in the Ag urban and/or commercial strata when Corelogic data was not used. More agricultural operations were found in the less than 15% cultivated strata when Corelogic data was used. Summary of Results To build our area frame, first a state is selected; in this case Pennsylvania. The counties in each state are divided into parcels of land usually about 6 to 8 square miles in size called Primary Sampling Units or PSUs. PSUs are then divided into 1 square mile parcel, called segments, from which we sample for the JAS. Finding and interviewing all farm operators can be challenging and costly, especially in previously unenumerated segments. In highly-cultivated land areas, names and addresses obtained from the Farm Service Agency (FSA) often provides good starting information to identify operators within the selected segment. In areas with small-scale agriculture, screening to identify farm operators is often time-consuming, expensive, and subject to misclassification. The JAS Faces Challenges When Screening Agricultural Operations A farm is any place from which $1,000 or more of agricultural products were produced and sold or normally would have been sold during the year. CoreLogic Experiment There were 11,085 total segments in 2012 JAS. 65.7% or 7,285 of the segments contained CoreLogic parcel data, where 5,925 were old segments, and 1,360 were new segments. A systematic stratified sample was selected. The treatment contained 60% or 4,371 segments and CoreLogic data were provided to enumerators. The control group contained 40% or 2,914 segments and no Corelogic data were provided. JAS segments sampled (experimental design; i.e., treatment vs. control) Strata were created based on:  Old segments vs. new segments,  Whether or not the segment contained FSA data, and  The segment’s percent of cultivation based on Satellite data (CDL) No t Significant EstimateStandard Errort ValuePr > |t| Region 1Treatment vs. Control-0.10320.1693-0.610.5421 Region 2Treatment vs. Control-0.12080.1715-0.700.4811 Region 3Treatment vs. Control-0.25100.1710-1.470.1422 Region 4Treatment vs. Control-0.22340.1724-1.300.1951 Region 5Treatment vs. Control-0.16640.1758-0.950.3441 Region 6Treatment vs. Control-0.27120.1778-1.530.1272 Least Squares Means of Treatment by CDL Group Least Squares Means of Treatment by Whether Segment Contained FSA data Least Squares Means of Treatment by AG Census Region Least Squares Means of Treatment by JAS Strata Sixth International Conference on Agricultural Statistics 23-25 October 2013 – Rio de Janeiro, Brazil