Key soil and topographic properties to delineate potential management classes for precision agriculture in the European loess area

Recent advances in on-the-go soil sensing, terrain modelling and yield mapping have made available large quantities of information about the within-field variability of soil and crop properties. But the selection of the key variables for an identification of management zones, required for precision agriculture, is not straightforward. To investigate a procedure for this selection, an 8 ha agricultural field in the Loess belt of Belgium was considered for this study. The available information consisted of: (i) top- and subsoil samples taken at 110 locations, on which soil properties: textural fractions, organic carbon (OC), CaCO₃ and pH were analysed, (ii) soil apparent electrical conductivity (EC_a) obtained through an electromagnetic induction based sensor, and (iii) wetness index, stream power index and steepest slope angle derived from a detailed digital elevation model (DEM). A principal component analysis, involving 12 soil and topographic properties and two EC_a variables, identified three components explaining 67.4% of the total variability. These three components were best represented by pH, EC_a that strongly associated with texture and OC. However, OC was closely related to some more readily obtainable topographic properties, and therefore elevation was preferred. A fuzzy k-means classification of these three variables produced four potential management classes. Three-year average standardized yield maps of grain and straw showed productivity differences across these classes, but mainly linked to their landscape position. In the loess area with complex soil-landscape interactions pH, EC_a and elevation can be considered as key properties to delineate potential management classes.     

Modelling Compaction from On-line Measurement of Soil Properties and Sensor Draught

The finite element method was used to analyse the cutting process of a sandy loam soil with medium-deep subsoiler, used as a compaction sensor, aiming to calculate the subsoiler draught for various combinations of dry bulk density, moisture content and tillage depth. The finite element results showed that draught increased with depth and dry bulk density, whereas it decreased with moisture content. A multiple linear regression analysis was performed to establish a formula for relating subsoiler draught (dependent variable) with the three independent variables. The regression equation developed was simple and had a high determination coefficient close to 0.95. An equation for prediction of dry bulk density as a function of moisture content, depth and draught was derived from the regression equation developed. This equation was used to calculate dry bulk density, for measured depth, moisture content and draught at nine different points along a single line in a meadow field of a silty clay loam soil. The predicted dry bulk density indicated that there was a considerable variation in the degree of compaction throughout the measured line. However, the on-line depth control and measurement of moisture content still need to be integrated with the on-line measurement of draught, to govern the model suitability for performing on-line detection of the spatial distribution of soil compaction, assessed as dry bulk density.     

Acoustic telemetry versus monitored longline fishing for studying the vertical distribution of pelagic fish: bigeye tuna (Thunnus obesus) in French Polynesia

This study compares detailed, nearly continuous, observations on bigeye tuna, Thunnus obesus equipped with electronic tags, with discrete observations on a larger number of individuals from fishing experiments in order to validate the use of instrumented longlines to study the vertical distribution of fish. We show that the depth distributions obtained from the two different observation techniques regarding different environmental variables (temperature, dissolved oxygen (DO), prey distribution) are similar. Bigeye tuna do not seem to be attracted by baits in the vertical dimension (no modification of their vertical distribution by the fishing gear), which allows the use of instrumented longlines to study the vertical behaviour of pelagic species. This technique, when used with appropriate deployment strategy, could therefore represent an alternative to electronic tags (acoustic or archival tags) when there is a need to determine the vertical distribution of fish species by size or sex, in different environments for the study of fishery interactions.     

Another Look at Bayesian Analysis of AMMI Models for Genotype-Environment Data

Linear–bilinear models are frequently used to analyze two-way data such as genotype-by-environment data. A well-known example of this class of models is the additive main effects and multiplicative interaction effects model (AMMI). We propose a new Bayesian treatment of such models offering a proper way to deal with the major problem of overparameterization. The rationale is to ignore the issue at the prior level and apply an appropriate processing at the posterior level to be able to arrive at easily interpretable inferences. Compared to previous attempts, this new strategy has the great advantage of being directly implementable in standard software packages devoted to Bayesian statistics such as WinBUGS/OpenBUGS/JAGS. The method is assessed using simulated datasets and a real dataset from plant breeding. We discuss the benefits of a Bayesian perspective to the analysis of genotype-by-environment interactions, focusing on practical questions related to general and local adaptation and stability of genotypes. We also suggest a new solution to the estimation of the risk of a genotype not exceeding a given threshold.     

Effects of bruise type on discrimination of bruised and non-bruised ‘Golden Delicious’ apples by VIS/NIR spectroscopy

Visual (VIS) and near-infrared (NIR) spectroscopy was applied to discriminate bruises and non-bruised healthy spots on ‘Golden Delicious’ apples. Two types of bruises were examined; those created by controlled impact and those by compression. Reflectance spectra of apples were measured in the range from 400 to 1700 nm. The data were analysed with canonical discriminant analysis (CDA). The squared canonical correlation (CR²) was 0.74 for discriminating impact bruises and non-bruised tissue, and a CR² of 0.68 was obtained for distinguishing compression bruises and sound tissue. Based on the linear discriminant functions, built with canonical components, the misclassification errors for non-bruised apples were mainly due to the presence of compression bruises. The classification accuracy was improved by taking the type of bruises into account in the model.