Ruth Kerry

Determining nugget:sill ratios of standardized variograms from aerial photographs to krige sparse soil data

Maps of kriged soil properties for precision agriculture are often based on a variogram estimated from too few data because the costs of sampling and analysis are often prohibitive. If the variogram has been computed by the usual method of moments, it is likely to be unstable when there are fewer than 100 data. The scale of variation in soil properties should be investigated prior to sampling by computing a variogram from ancillary data, such as an aerial photograph of the bare soil. If the sampling interval suggested by this is large in relation to the size of the field there will be too few data to estimate a reliable variogram for kriging. Standardized variograms from aerial photographs can be used with standardized soil data that are sparse, provided the data are spatially structured and the nugget:sill ratio is similar to that of a reliable variogram of the property. The problem remains of how to set this ratio in the absence of an accurate variogram. Several methods of estimating the nugget:sill ratio for selected soil properties are proposed and evaluated. Standardized variograms with nugget:sill ratios set by these methods are more similar to those computed from intensive soil data than are variograms computed from sparse soil data. The results of cross-validation and mapping show that the standardized variograms provide more accurate estimates, and preserve the main patterns of variation better than those computed from sparse data.

Editorial note for the special issue on spatial variation

Spatial variation in crop, weed and soil properties within fields is an important aspect of precision agriculture. This special issue of Precision Agriculture brings together a set of papers based on presentations given at the stimulating Sixth European Conference on Precision Agriculture held on the island of Skiathos, Greece in June 2007. These papers address a variety of important topics on spatial variation in precision agriculture. The first two papers by Wong et al. and Castrignano et al. won awards for the best papers (three in total) given by Springer at the conference. The papers by Kerry and Oliver and Wetterlind et al. relate to the costs of field sampling to characterize spatial variation in soil accurately. This is largely a function of the number of samples that are taken and analyzed. The papers by Inman et al. and Kyaw et al. describe the delineation and use of management zones as a means of site-specific management to provide economic and environmental benefits. The final paper by Tisseyre and McBratney describes an index to determine whether precision farming is technically appropriate. The paper by Wong et al. ‘‘Mapping subsoil acidity and shallow soil across a field with information from yield maps, geophysical sensing and the grower’’, compares different methods of identifying subsoil acidity and shallow soil based on information farmers might or might not have. They used simulation to determine the weather conditions under which subsoil acidity and shallow rooting depth begin to limit yield. Spatial variation in yield from key years was used to determine where these problems exist in the field. They explain how grower information and geophysical surveys can both provide valuable insight into the problems. Castrignano et al. in ‘‘Multi-scale assessment of the risk of soil salinization in an area of south-eastern Sardinia (Italy)’’ investigate how the scale of spatial variation can be used to decide who is responsible for determining where problems of salinity exist and its remediation. The decision to implement precision farming methods is made by individual private sector farmers or agri-businesses, whereas, national and local public sector bodies

A comparison of multiple indicator kriging and area-to-point Poisson kriging for mapping patterns of herbivore species abundance in Kruger National Park, South Africa

Kruger National Park (KNP), South Africa, provides protected habitats for the unique animals of the African savannah. For the past 40 years, annual aerial surveys of herbivores have been conducted to aid management decisions based on (1) the spatial distribution of species throughout the park and (2) total species populations in a year. The surveys are extremely time consuming and costly. For many years, the whole park was surveyed, but in 1998 a transect survey approach was adopted. This is cheaper and less time consuming but leaves gaps in the data spatially. Also the distance method currently employed by the park only gives estimates of total species populations but not their spatial distribution. We compare the ability of multiple indicator kriging and area-to-point Poisson kriging to accurately map species distribution in the park. A leave-one-out cross-validation approach indicates that multiple indicator kriging makes poor estimates of the number of animals, particularly the few large counts, as the indicator variograms for such high thresholds are pure nugget. Poisson kriging was applied to the prediction of two types of abundance data: spatial density and proportion of a given species. Both Poisson approaches had standardized mean absolute errors (St. MAEs) of animal counts at least an order of magnitude lower than multiple indicator kriging. The spatial density, Poisson approach (1), gave the lowest St. MAEs for the most abundant species and the proportion, Poisson approach (2), did for the least abundant species. Incorporating environmental data into Poisson approach (2) further reduced St. MAEs.

Investigating the Potential of Area-to-Area and Area-to-Point Kriging for Defining Management Zones for Precision Farming of Cranberries

Cranberries are harvested by flooding the field and agitating vines so the fruit, which float can be skimmed from the surface and loaded into barrels. This harvesting method makes application of standard precision farming practices difficult. This paper investigates the potential of combining Area-to-Area (AtoA) and Area-to-Point (AtoP) kriging of yield totals from individual fields with remotely sensed data for defining within-field management zones.

Spatial analysis of drug poisoning deaths in the American West, particularly Utah.

BACKGROUND Most states in the Western US have high rates of drug poisoning death (DPD), especially New Mexico, Nevada, Arizona and Utah (UT). This seems paradoxical in UT where illicit drug use, smoking and drinking rates are low. To investigate this, spatial analysis of county level DPD data and other relevant factors in the Western US and UT was undertaken. METHODS Poisson kriging was used to smooth the DPD data, populate data gaps and improve the reliability of rates recorded in sparsely populated counties. Links between DPD and economic, environmental, health, lifestyle, and demographic factors were investigated at four scales using multiple linear regression. LDS church membership and altitude, factors not previously considered, were included. Spatial change in the strength and sign of relationships was investigated using geographically weighted regression and significant DPD clusters were identified using the Local Morans I. RESULTS Economic factors, like the sharp social gradient between rural and urban areas were important to DPD throughout the west. Higher DPD rates were also found in areas of higher elevation and the desert rural areas in the south. The unique characteristics of DPD in UT in terms of health and lifestyle factors, as well as the demographic structure of DPD in the most LDS populous states (UT, Idaho, Wyoming), suggest that high DPD in heavily LDS areas are predominantly prescription opioid related whereas in other Western states a larger proportion of DPD might come from illicit drugs. CONCLUSION Drug policies need to be adapted to the geographical differences in the dominant type of drug causing death. Educational materials need to be marketed to the demographic groups at greatest risk and take into account differences in population characteristics between and within States. Some suggestions about how such adaptations can be made are given and future research needs outlined.

Monitoring and assessment of soil erosion at micro-scale and macro-scale in forests affected by fire damage in northern Iran

Understanding the occurrence of erosion processes at large scales is very difficult without studying them at small scales. In this study, soil erosion parameters were investigated at micro-scale and macro-scale in forests in northern Iran. Surface erosion and some vegetation attributes were measured at the watershed scale in 30 parcels of land which were separated into 15 fire-affected (burned) forests and 15 original (unburned) forests adjacent to the burned sites. The soil erodibility factor and splash erosion were also determined at the micro-plot scale within each burned and unburned site. Furthermore, soil sampling and infiltration studies were carried out at 80 other sites, as well as the 30 burned and unburned sites, (a total of 110 points) to create a map of the soil erodibility factor at the regional scale. Maps of topography, rainfall, and cover-management were also determined for the study area. The maps of erosion risk and erosion risk potential were finally prepared for the study area using the Revised Universal Soil Loss Equation (RUSLE) procedure. Results indicated that destruction of the protective cover of forested areas by fire had significant effects on splash erosion and the soil erodibility factor at the micro-plot scale and also on surface erosion, erosion risk, and erosion risk potential at the watershed scale. Moreover, the results showed that correlation coefficients between different variables at the micro-plot and watershed scales were positive and significant. Finally, assessment and monitoring of the erosion maps at the regional scale showed that the central and western parts of the study area were more susceptible to erosion compared with the western regions due to more intense crop-management, greater soil erodibility, and more rainfall. The relationships between erosion parameters and the most important vegetation attributes were also used to provide models with equations that were specific to the study region. The results of this paper can be useful for better understanding erosion processes at the micro-scale and macro-scale in any region having similar vegetation attributes to the forests of northern Iran.

Investigating temporal and spatial patterns of cranberry yield in New Jersey fields

Cranberries are grown in sensitive wetland ecosystems and precision farming could be beneficial to reduce agro-chemical pollution and increase production without expanding area. Precision farming requires knowledge of the variation of yield within-fields but cranberry harvesting methods produce only one yield value per field unless an expensive pre-harvest berry count is done. Co-operatives and extension services have an important role in precision farming to: (1) determine important factors affecting yield patterns within a growing region and (2) identify fields that would benefit most from future intensive survey. This paper reports a study to investigate temporal and spatial patterns in useable and poor quality cranberry yield for the New Jersey (NJ), USA growing region. Principal components analysis indicated that mean growing season temperature is important for understanding temporal patterns in useable yield and maximum temperatures and precipitation for poor quality yield. Multiple linear regression showed that some cultivars were susceptible to disease and poor quality yield in years with high maximum growing season temperatures. Analysis of spatial patterns using area to area and area to point kriging, local cluster analysis and geographically weighted regression helped identify clusters of fields that were consistently yielding or alternated between high and low yielding. They also showed differences between owners and soil types particularly in hot or wet years showing the different response to soil types to weather and the potential for improvement in irrigation practices by some owners. The methods used should be useful for other growing regions and crops, particularly where there are no yield monitors.

Investigating geostatistical methods to model within-field yield variability of cranberries for potential management zones

Cranberry harvesting methods give only one yield value per field making characterization of within-field variation, the usual first step in precision farming, difficult. Time-consuming berry count yield and fruit rot estimations are the best “ground truth” indication of yield variation within fields. Correlations and coincidence of binary classifications based on less expensive methods such as enhanced vegetation index (EVI) from imagery, and area to point (AtoP) kriging of useable, poor quality and trash yields were compared with this “ground truth”. In general AtoP kriged values gave higher correlations and kappa statistic values with berry counts and fruit rot than EVI. Geostatistical disaggregation of per field yield totals using AtoP kriging with EVI as an external drift (AtoPKED) was also investigated. Factorial kriging was used to separate the several scales of variation in “ground truth” and EVI data and determine which ones were most spatially coherent/manageable and which related best to the AtoP kriged data. The spatial trend component of pre-harvest berry counts and AtoP kriging of yields both gave a good initial definition of spatially coherent, relatively permanent management zones. They were related to topography and depth of water table in the soil which are key factors governing cranberry yield. AtoP kriging or AtoPKED are recommended for defining management zones as they are less expensive than berry counts. The value of AtoP kriging to precision farmers for other crops to map soils at the farm scale with some imagery and just one bulked soil sample per field or use nutrient levels associated with each polygon of traditional soil survey maps is discussed in the conclusions.

A Web-based GIS Decision Support Tool for Determining Corn Aflatoxin Risk: A Case Study Data from Southern Georgia, USA

Aflatoxin is a fungal toxin contaminating corn and causing liver cancer in humans and animals. Contamination is driven by high temperatures and drought. Aflatoxin assessment is expensive so extension services need to identify high risk areas so irrigation, planting strategies and corn varieties can be adapted. This research presents a web-based decision support tool for risk illustrated with a case study from southern Georgia. The tool employs the approach, developed by Kerry et al. (2017b) where exceedance of key thresholds in temperatures, rainfall, soil type and corn production are used to determine risk. The tool also includes NDVI to indicate drought stress and could be further expanded to include new risk factors and adapted to other crops.

Investigating geostatistical methods to model within-field yield variability of cranberries

The method of harvesting cranberries gives just one yield value per field so characterizing within-field variation is difficult. Geostatistical disaggregation of per field yield totals using the enhanced vegetation index (EVI) from imagery as secondary information was investigated. Results were compared to within-field yield variability projected from time-consuming pre-harvest berry counts. Several scales of variation were present in the data so factorial kriging was used to separate these and determine which are most spatially coherent. The trend component of pre-harvest berry counts and/or geostatistical disaggregation of yields both give a reasonable initial definition of potential management zones likely to be related to topography and soil type differences.