April 2002

A Comparison of Directed Crop Scouting and Traditional Sampling

Key words: remote sensing, directed sampling, spatial statistics

Update on Directed Crop Scouting: Issue 2 of 2

G.K. Blumhoff

Choosing different sampling methods and mapping algorithms can be can be a challenging task. The basic goal is to derive an informative map of field conditions without having to scour every inch of the field. A directed approach to crop scouting can provide valuable information about the field without intense sampling requirements. This method lets the field do the talking. However, depending on the method, bias can result, producing a skewed view of actual field conditions. Thus, psuedo-random approaches have been advocated to remove this problem. This type of sampling may also produce errors due to sparse sample numbers and distorted maps not representative of actual field conditions. This article summarizes a research project which evaluated both types of sampling in order to decide which method is more useful when applied to site-specific requirements.

Factors such as efficiency and quality of information obtained from traditional crop scouting methods and the proposed directed method were compared in this study. Specifically, tradeoffs exist between increases in the density of sampling, labor and the expense to collect and analyze samples, and the resulting accuracy of maps to represent the spatial patterns in crop conditions observed. Existing methods address identification and control thresholds, but do not specifically address the "How to?" aspects of crop scouting related the "location of" and/or "extent of" yield influencing factors occurring in the field.

The March newsletter provided a summary on an alternative method of crop scouting tailored to precision agriculture applications. Directed sampling involves the use of remotely sensed image data to guide or target sampling. Research efforts were centered on testing the feasibility of existing sampling methods (psuedo-random or zigzag sampling) and targeted sampling with the use of secondary information such as yield maps, soil types, and/or remote sensing. Remote sensing offers the potential for identifying small-scale spatial patterns in crop conditions, crop yield patterns across the field, and optimizing crop scouting methods to quantify those problem areas.

The crop scouting method comparison was located on about 250 acres of corn and soybeans located on two local growers farms in west central Indiana. The procedure described in the March newsletter was used to direct sampling within the corn and soybean fields. Traditional sampling or zigzag sampling was also performed within these fields to provide to separate data sets to compare. Variability maps were produced from the sample results and compared to a map produced from pooled (intensive, directed, and zigzag data). Map production was driven by sample density, data relationships, and map quality that could be achieved. Three different map production algorithms were used to produce maps. They included ordinary kriging (spatial statistics), normal matching (basic statistics), and inverse distance weighting (common default). Although complicated and somewhat subjective in nature, ordinary kriging was selected because it has the ability to produce informative results given sparse data. Normal matching is an older method that involved data transformation. Basically, it requires that there be a significant relationship between remote sensing data and data collected by crop scouts on the ground. This method produces a spatially continuous map of individual pest variability based on sparse ground collected data and dense remote sensing data. Lastly, inverse distance weighting was incorporated as a contingency in cases when the other methods guidelines could not be satisfied.

Example maps are provided to express the differences between directed, zigzag (psuedo-random), and intensive datasets. Figure 1 and Figure 2 show examples of map results obtained for each of the crop scouting methods. The maps are presented with directed sampling results on the left, pooled data in the middle, and traditional zigzag sampling results on the right. The main variations between the methods are in the detail of ground sampled data. An important detail to observe is related to spatial detail and the ability to categorize the data for future management applications. Also, it is important to recognize the quality of the estimates produced regarding true variability and artificial results.

Figure 1 shows plant height in a 120-acre cornfield collected in mid-August. The number of samples collected for each method included: 54 samples for zigzag, 51 samples for directed, and a total of 105 samples to pool for comparison. In this case, sample data observed by both methods were added together to produce a higher density dataset to compare the two sampling methods. An aerial sensor acquired the remotely sensed data on August 14, 2000 at 1^1/2 meter spatial resolution. The relationship between plant height and the image data maintained a high R² value of .65. This relationship met the normal matching requirements of > .35 R².The results verify the limitations of the traditional or zigzag crop scouting method. Only a small portion of field variability was identified with zigzag estimates when compared to interpolated map results shown in the pooled data. Also, class boundaries appear somewhat artificial, including a lack of detail in areas of the field where the shortest plants were located. This displays the limitations of the method regarding the identification of the area and extent of potentially important crop limiting factors occurring in the field.

Figure 2 shows map surfaces of estimated yield from ground collected data for a 65-acre soybean field in mid-August. The number of samples collected for each method included: 16 samples for zigzag, 18 samples for directed, and a total of 71 samples to pool for comparison. Note that the pooled dataset contains a larger sample number. For research purposes, data were collected at additional sample locations to provide greater detail in map results. In some cases, data sparsity prevented the use of spatial analysis, thus requiring greater sample numbers. An aerial sensor acquired the remotely sensed data on August 14, 2000 at 1^1/2 meter spatial resolution. The relationship between estimated hand-yield and the image data maintained a high R² value of .51, which satisfied the normal matching requirements. Similar to results displayed in Figure 1, the map produced from zigzag data shows artificial "bulls-eye" patterns with a large amount of smoothing. Further, it also shows problems that the method has related to over/under-estimation of yield variability.

The directed or targeted sampling method has the potential to improve site-specific pest management strategies. Further, this method could also impact agriculture related to other crop production parameters and promote improved environmental quality.

Zigzag: Ordinary Kriging

Figure 1. Map surfaces (raster data) produced from plant height sample data collected August 14, 2000.

Figure 2. Map surfaces of SB1 field produced from estimated hand-yield data. SB1 field produced sample data collected August 1-5, 2000. Areas with a low reading are those exhibiting a stress or deficiency.

For more information:

Blumhoff, Greg K., 2002. Spatial Variability Assessment With Remote Sensing and Directed Sampling. M.S. thesis. Purdue University, West Lafayette, IN.