Search

The specific objective of this study was to use a logistic regression model for determining the degree to which egg laying patterns of individual females at the end of life (i.e., terminal segments) in each of three different fruit fly species could be distinguished from the egg-laying patterns over a similar period in midlife (i.e., non-terminal segments). Extracting data from large-scale databases for 11-day terminal and 11-day non-terminal segments in the vinegar fly (Drosophila melanogaster), the Mexican fruit fly (Anastrepha ludens) and the Mediterranean fruit fly (Ceratitis capitata) and organizing the model's results in a 2 × 2 contingency table, we found that: (1) daily egg-laying patterns in fruit flies can be used to distinguish terminal from non-terminal periods; (2) the overall performance metrics such as precision, accuracy, false positives and true negatives depended heavily on species; (3) differentiating between terminal and non-terminal segments is more difficult when flies die at younger ages; and (4) among the three species the best performing metrics including accuracy and precision were those produced using data on D. melanogaster. We conclude that, although the reliability of the prediction of whether a segment occurred at the end of life is relatively high for most species, it does not follow precisely predicting remaining life will also be highly reliable since classifying an end of life period is a fundamentally different challenge than is predicting an exact day of death.

Context. Unreplicated trials are common in agriculture. However, statistical inferences differ from those of traditional experiments based on small, replicated plots. Aims. To present a method to assess management effects on soil carbon (C) storage from unreplicated, side-by-side field trials. Methods. Two estimates of means with spatially correlated errors are compared using a corrected t-statistic. Then causal inference is made by analysing a significant difference between the means (P < 0.05) and its changes over time. The use of the method is described in comparing yield and organic C stocks between two large fields. Yield was measured during 1997-2005 with a commercial yield monitor, and soil organic C stocks during 2003-2005. The fields experienced the same tillage practice until autumn 2003 and then with different tillage intensity. Key results. The results show that crop C yield did not differ between the fields when using the same tillage practice but was greater in the tilled than the no-till field. The results also suggest that total and particulate organic matter-C contents depend on tillage history. For comparative purposes, the data were also analysed using standard mixed model analysis with a semivariogram model for spatial autocorrelation among the residuals. The mixed model results were generally similar to those of the corrected t-statistic method. The mixed model was often, but not always, less conservative than the corrected t-statistic model. Conclusions. The method allows analysis of whole-field data and improves our understanding of soil C processes in commercial fields, where agricultural assessment cannot involve replication due to agronomic and economic constraints. Implications. The method complements observational data analyses and can offer a direction towards whole-field management. Keywords: crop management, crop yields, observational data, particulate organic carbon, precision agriculture, soil carbon dynamics, soil management, spatial model, tillage, total carbon, unreplicated trials, yield monitor., Introduction Agricultural practices alter soil conditions and crop characteristics that are spatially dependent (Hobbs 2003; Lark and Wheeler 2003). For example, tillage and residue management can affect the amount, placement [...]

The strategic management of agricultural lands involves crop field monitoring each year. Crop discrimination via remote sensing is a complex task, especially if different crops have a similar spectral response and cropping pattern. In such cases, crop identification could be improved by combining object-based image analysis and advanced machine learning methods. In this investigation, we evaluated the C4.5 decision tree, logistic regression (LR), support vector machine (SVM) and multilayer perceptron (MLP) neural network methods, both as single classifiers and combined in a hierarchical classification, for the mapping of nine major summer crops (both woody and herbaceous) from ASTER satellite images captured in two different dates. Each method was built with different combinations of spectral and textural features obtained after the segmentation of the remote images in an object-based framework. As single classifiers, MLP and SVM obtained maximum overall accuracy of 88%, slightly higher than LR (86%) and notably higher than C4.5 (79%). The SVM+SVM classifier (best method) improved these results to 89%. In most cases, the hierarchical classifiers considerably increased the accuracy of the most poorly classified class (minimum sensitivity). The SVM+SVM method offered a significant improvement in classification accuracy for all of the studied crops compared to the conventional decision tree classifier, ranging between 4% for safflower and 29% for corn, which suggests the application of object-based image analysis and advanced machine learning methods in complex crop classification tasks.

Since 1954, when the first tropical tephritid fruit fly was detected in California, a total of 17 species in four genera and 11 386 individuals (adults/larvae) have been detected in the state at more than 3348 locations in 330 cities. We conclude from spatial mapping analyses of historical capture patterns and modelling that, despite the 250+ emergency eradication projects that have been directed against these pests by state and federal agencies, a minimum of five and as many as nine or more tephritid species are established and widespread, including the Mediterranean, Mexican and oriental fruit flies, and possibly the peach, guava and melon fruit flies. We outline and discuss the evidence for our conclusions, with particular attention to the incremental, chronic and insidious nature of the invasion, which involves ultra-small, barely detectable populations. We finish by considering the implications of our results for invasion biology and for science-based invasion policy.

Our current understanding of the mechanisms driving spatiotemporal yield variability in rice systems is insufficient for effective management at the sub-field scale. The overall objective of this study was to evaluate the potential of precision management for rice production. The spatiotemporal properties of multiyear yield monitor data from four rice fields, representing varying soil types and locations within the primary rice growing region in California, were quantified and characterized. The role of water management, land-leveling, and the spatial distribution of soil properties in driving yield heterogeneity was explored. Mean yield and coefficient of variation at the sampling points within each field ranged from 9.2 to 12.1 Mg ha-1 and from 7.1 to 14.5 %, respectively. Using a k-means clustering and randomization method, temporally stable yield patterns were identified in three of the four fields. Redistribution of dissolved organic carbon, nitrogen, potassium and salts by lateral flood water movement was observed across all fields, but was only related to yield variability via exacerbating areas with high soil salinity. The effects of cold water temperature and land-leveling on yield variability were not observed. Soil electrical conductivity and/or plant available phosphorus were identified as the underlying causes of the within-field yield patterns using classification and regression trees. Our results demonstrate that while the high temporal yield variability in some rice fields does not permit precision management, in other fields exhibiting stable yield patterns with identifiable causes, precision management and modified water management may improve the profitability and resource-use efficiency of rice production systems. © 2013 Springer Science+Business Media New York.

Spatial autocorrelation of biological process has been shown to reduce experimental precision in field experiments, but horticultural researchers rarely consider this effect when designing their experiments. We tested how spatial autocorrelation affects the precision of orchard experiments, measured as Type I error rate, by simulating 40000 experiments based on a data-set gathered of 36000 individual tree yields over four years (4000 iterations of ten plot layouts in each year). Spatial autocorrelation varied among years and was a significant factor in the precision of experiments. Block designs were generally less effective than grid-based or random designs at controlling Type I error rate. Designs based on large blocks (twenty-seven trees) or small blocks (three trees) were robust against the effects of spatial autocorrelation. More moderately sized block designs (nine trees) and blocks based on rows of trees–designs common in tree crop research–performed the most poorly. These findings clearly demonstrated that spatial autocorrelation is a significant factor in the precision of tree crops and therefore might cause spurious experimental results if not explicitly taken into account.

The author advises that the funding information was incorrectly specified in the above paper. The correct information is provided below. Declaration of funding. This work was supported by a Gyeongsang [...]