Ground-level hyperspectral imagery for detecting weeds in wheat fields

Site-specific weed management can allow more efficient weed control from both an environmental and an economic perspective. Spectral differences between plant species may lead to the ability to separate wheat from weeds. The study used ground-level image spectroscopy data, with high spectral and spatial resolutions, for detecting annual grasses and broadleaf weeds in wheat fields. The image pixels were used to cross-validate partial least squares discriminant analysis classification models. The best model was chosen by comparing the cross-validation confusion matrices in terms of their variances and Cohen’s Kappa values. This best model used four classes: broadleaf, grass weeds, soil and wheat and resulted in Kappa of 0.79 and total accuracy of 85 %. Each of the classes contains both sunlit and shaded data. The variable importance in projection method was applied in order to locate the most important spectral regions for each of the classes. It was found that the red-edge is the most important region for the vegetation classes. Ground truth pixels were randomly selected and their confusion matrix resulted in a Kappa of 0.63 and total accuracy of 72 %. The results obtained were reasonable although the model used wheat and weeds from different growth stages, acquisition dates and fields. It was concluded that high spectral and spatial resolutions can provide separation between wheat and weeds based on their spectral data. The results show feasibility for up-scaling the spectral methods to air or spaceborne sensors as well as developing ground-level application.     

Transoceanic migration, spatial dynamics, and population linkages of white sharks

The large-scale spatial dynamics and population structure of marine top predators are poorly known. We present electronic tag and photographic identification data showing a complex suite of behavioral patterns in white sharks. These include coastal return migrations and the fastest known transoceanic return migration among swimming fauna, which provide direct evidence of a link between widely separated populations in South Africa and Australia. Transoceanic return migration involved a return to the original capture location, dives to depths of 980 meters, and the tolerance of water temperatures as low as 3.4 degrees C. These findings contradict previous ideas that female white sharks do not make transoceanic migrations, and they suggest natal homing behavior.     

Development of early-flowering Kabuli chickpea with compound and simple leaves

Terminal drought is a major constraint to chickpea (Cicer arietinum L.) production. Autumn sowing and early flowering have been suggested as ways to benefit from the winter rains in short rainy seasons under dryland cropping. High‐yielding, late‐flowering, simple‐leafed (slv/slv) chickpea cultivars with good field resistance to Ascochyta blight have been bred recently. Changing plant architecture, by altering leaf shape, may affect agronomic performance. As no information is available on the effect of leaf shape on phenology and seed yield, this study was aimed at: (i) introducing the simple leaf trait into an early‐flowering chickpea background; (ii) comparing the grain yield of the two leaf types in early vs. late flowering backgrounds and (iii) producing breeding lines combining early flowering, large seeds and Ascochyta tolerance with both leaf types. Hybrid progeny were studied from the cross of ‘Sanford’ (slv/slv) and ICC7344, (compound, SLV/SLV). Four early‐podding, F₈ breeding lines were selected with either simple or compound leaves. In three different field experiments under dryland conditions (334–379 mm), they yielded ca. 1.4 t/ha as compared with 1.0 t/ha in the standard Israeli ‘Yarden’ on one site, but no significant differences in yield were obtained in the other two experiments.     

Leaf shape × sowing density interaction affects chickpea grain yield

Modifying plant architecture is considered a promising breeding option to enhance crop productivity. Modern chickpea (Cicer arietinum L.) cultivars with either compound (wild‐type) or simple leaf shapes are commercially grown but the relationships between leaf shape and yield are not well understood. In this study, a random sample of ‘Kabuli’ type progeny lines of both leaf types, derived from two crosses between modern American simple leaf cultivars and early‐flowering wild‐type breeding lines, were planted at different sowing densities. Leaf area development and final grain yield in genotypes of the two leaf types responded differently to changes in sowing densities. Compound leaf lines attained higher leaf area indices and higher grain yields at both low and high sowing densities. Yield responses of the simple leaf lines to increasing sowing density were significantly higher compared to compound leaf genotypes in two of three field experiments. The prospects for utilizing the simple leaf trait as a breeding target for short‐season growing areas are discussed.     

Bioassay to Forecast Cereal Cyst Nematode Damage to Wheat in Fields

Several simple agronomic practices such as crop rotation, timing of sowing, no-till management, and resistant cultivars can reduce damage by the cereal cyst nematode (CCN), (Heterodera avenae), but the damage levels are unpredictable. The objective of the present study was to develop a fast assay that would forecast the potential damage to wheat by CCN in the fields. Our forecast procedure was based on the SIRONEM bioassay with several modifications in the sampling and incubation of field soil samples and cultivation in them of wheat seedlings. For CCN disease assessment, a damage model that takes account of CCN infection and its damage to roots was developed. Field experiments were established to validate the resulting damage model, and showed a correlation between the forecast damage and field rating of CCN infection. This assay has advantages over other CCN estimation methods because it directly forecasts the potential crop damage in the field and it requires a relatively short period to obtain the data. According to the forecast damage and weather conditions, the most appropriate crop management regime, especially of sowing, can be chosen for each field assayed.     

Leaf nitrogen estimation in potato based on spectral data and on simulated bands of the VENμS satellite

Relationships between leaf spectral reflectance at 400–900 nm and nitrogen levels in potato petioles and leaves were studied. Five nitrogen (N) fertilizer treatments were applied to build up levels of nitrogen variation in potato fields in Israel in spring 2006 and 2007. Reflectance of leaves was measured in the field over a spectral range of 400–900 nm. The leaves were sampled and analyzed for petiole NO₃–N and leaf percentage N (leaf-%N). Prediction models of leaf nitrogen content were developed based on an optical index named transformed chlorophyll absorption reflectance index (TCARI) and on partial least squares regression (PLSR). Prediction models were also developed based on simulated bands of the future VENμS satellite (Vegetation and Environment monitoring on a New Micro-Satellite). Leaf spectral reflectance correlated better with leaf-%N than with petiole NO₃–N. The TCARI provided strong correlations with leaf-%N, but only at the tuber-bulking stage. The PLSR analysis resulted in a stronger correlation than TCARI with leaf-%N. An R ² of 0.95 (p < 0.01) and overall accuracy of 80.5% (Kappa = 74%) were determined for both vegetative and tuber-bulking periods. The simulated VENμS bands gave a similar correlation with leaf-%N to that of the spectrometer spectra. The satellite has significant potential for spatial analysis of nitrogen levels with inexpensive images that cover large areas every 2 days.     

Exploring remotely sensed technologies for monitoring wheat potassium and phosphorus using field spectroscopy

Given the importance of potassium (K) and phosphorus (P) contents to wheat yield and grain quality, and the very little experience that has been gained on nutritional monitoring of other than nitrogen using remotely sensed technologies, a study was undertaken to explore the possibility of identifying these mineral stresses using spectral data. Canopy spectra and biophysical data were collected from commercial and experimental fields in India and Israel. Traditional and newly developed vegetation indices, together with Partial Least Squares (PLS) regression models, were calculated in order to predict potassium and phosphorus contents from the wheat canopy spectral data. Results show that the application of PLS and specific narrow bands vegetation indices reached significant levels of accuracy in the retrieval of K and P levels, in comparison to traditional broad band indices. Additionally, it was observed that a significant improvement is obtained when the mineral total content is considered instead of the relative content. Therefore it was suggested that the biomass should also be retrieved from the spectral data. Finally, as very different crop conditions were included in this study, it was possible to confirm that the level of accuracy in the retrieval of K and P levels is related to the quality and variability of the data used for calibrating the models.     

Biometric Analyses of the Inheritance of Resistance to Didymella rabiei in Chickpea

ABSTRACT Historically, the response of chickpea (Cicer arietinum L.) to Didymella rabiei (causal agent of Ascochyta blight) has been mainly related to as complete resistance and it was commonly assayed with qualitative (nonparametric) scales. Two reciprocal populations, derived from intra-specific crosses between a moderately resistant late flowering Israeli cultivar and a highly susceptible early flowering Indian accession, were tested at F(3) and F(4) generations in 1998 and 1999, respectively. A quantitative (parametric) assessment (percent disease severity) was used to evaluate the chickpea field response to Ascochyta blight. The transformed relative area under the disease progress curve (tRAUDPC) was calculated for each experimental unit for further analyses. Heritability estimates of the tRAUDPC were relatively high (0.67 to 0.85) in both generations for both reciprocal populations. The frequency distributions of tRAUDPC of the populations were continuous and significantly departed from normality (Shapiro-Wilk W test; P of W < 0.0001), being all platykurtic and skewed toward either the resistant or the susceptible parental lines. The presence of major genes was examined by testing the relationship between the F(3) and F(4) family means and the within-family variances (Fain's test). Analyses of these relationships suggested that segregation of a single (or few) quantitative trait locus with major effect and possibly other minor loci was the predominant mode of inheritance. The correlation estimates between the resistance and days to flower (r = -0.19 to -0.44) were negative and significantly (P = 0.054 to 0.001) different from zero, which represents a breeding constraint in the development of early flowering cultivars with Ascochyta blight resistance.     

A spectral index to monitor the head-emergence of wheat in semi-arid conditions

Harvesting wheat (Triticium aestivum L.) for forage or leaving it for grain is the main decision uncertainty growers face in semi-arid regions during mid-season. To facilitate decision-making, a decision support system (DSS) has recently been proposed that requires information about crop water and nutritional status during spike emergence. Though remote sensing has been used to provide site-specific crop status information, a spectral vegetation index is needed to ensure that the information has been acquired during spike emergence. The objective of this study was to propose a spectral index sensitive to spike emergence and validate its suitability across different commercial farm fields by using ground spectral measurements and multispectral satellite imagery. To develop the index, controlled experiments with commonly grown wheat varieties were conducted during the 2004/2005 and 2005/2006 growing season in the agricultural area of the northern Negev desert of Israel. The experiments showed that spike emergence correlated most strongly (r = 0.7, p < 0.05) with spectral changes near the 1.2 μm water absorption feature in contrast to the band at 1.1 μm which appeared to be only weakly correlated. Thus, the spike emergence sensitive band at 1.2 μm has been combined with the insensitive band at 1.1 μm as reference to form the ratio-based normalized heading index (NHI). Experimental data were then used to establish an index threshold that helps separate data acquired before and after spike emergence. The proposed NHI was able to identify spike emergence with a classification accuracy varying between 53 and 83%. Accuracy was influenced by season, and whether narrow or broad spectral bands were used. Validation of the index in commercial farm fields in Israel and the United States showed that the classification accuracy was similar for ground spectral measurements and the advanced land imager (ALI) satellite imagery. These results suggest that the NHI is suited for identifying the onset of heading throughout wheat-growing areas without the need for characterizing seasonal trends.