Assessing the relationship between shire winter crop yield and seasonal variability of the MODIS NDVI and EVI images

Australian researchers have been developing robust yield estimation models, based mainly on the crop growth response to water availability during the crop season. However, knowledge of spatial distribution of yields within and across the production regions can be improved by the use of remote sensing techniques. Images of Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices, available since 1999, have the potential to contribute to crop yield estimation. The objective of this study was to analyse the relationship between winter crop yields and the spectral information available in MODIS vegetation index images at the shire level. The study was carried out in the Jondaryan and Pittsworth shires, Queensland , Australia . Five years (2000 to 2004) of 250m resolution, 16-day composite of MODIS Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) images were used during the winter crop season (April to November). Seasonal variability of the profiles of the vegetation index images for each crop season using different regions of interest (cropping mask) were displayed and analysed. Correlation analysis between wheat and barley yield data and MODIS image values were also conducted. The results showed high seasonal variability in the NDVI and EVI profiles, and the EVI values were consistently lower than those of the NDVI. The highest image values were observed in 2003 (in contrast to 2004), and were associated with rainfall amount and distribution. The seasonal variability of the profiles was similar in both shires, with minimum values in June and maximum values at the end of August. NDVI and EVI images showed sensitivity to seasonal variability of the vegetation and exhibited good association (e.g. r = 0.84, r = 0.77) with winter crop yields.

Detecting the attributes of a wheat crop using digital imagery acquired from a low-altitude platform

A low-altitude platform utilising a 1.8-m diameter tethered helium balloon was used to position a multispectral sensor, consisting of two digital cameras, above a fertiliser trial plot where wheat (Triticum spp.) was being grown. Located in Cecil Plains, Queensland, Australia, the plot was a long-term fertiliser trial being conducted by a fertiliser company to monitor the response of crops to various levels of nutrition. The different levels of nutrition were achieved by varying nitrogen application rates between 0 and 120 units of N at 40 unit increments. Each plot had received the same application rate for 10 years. Colour and near-infrared images were acquired that captured the whole 2 ha plot. These images were examined and relationships sought between the captured digital information and the crop parameters imaged at anthesis and the at-harvest quality and quantity parameters. The statistical analysis techniques used were correlation analysis, discriminant analysis and partial least squares regression. A high correlation was found between the image and yield (R2 = 0.91) and a moderate correlation between the image and grain protein content (R2 = 0.66). The utility of the system could be extended by choosing a more mobile platform. This would increase the potential for the system to be used to diagnose the causes of the variability and allow remediation, and/or to segregate the crop at harvest to meet certain quality parameters.

Possible impacts of the invasive plant Rubus niveus on the native vegetation of the Scalesia Forest in the Galapagos Islands

Originally from Asia, Rubus niveus has become one of the most widespread invasive plant species in the Galapagos Islands. It has invaded open vegetation, shrubland and forest alike. It forms dense thickets up to 4 m high, appearing to displace native vegetation, and threaten the integrity of several native communities. This study used correlation analysis between a R. niveus cover gradient and a number of biotic (vascular plant species richness, cover and vegetation structure) and abiotic (light and soil properties) parameters to help understand possible impacts in one of the last remaining fragments of the Scalesia forest in Santa Cruz Island, Galapagos. Higher cover of R. niveus was associated with significantly lower native species richness and cover, and a different forest structure. Results illustrated that 60% R. niveus cover could be considered a threshold for these impacts. We suggest that a maximum of 40% R. niveus cover could be a suitable management target.

Preliminary assessment of a rapid leaf nitrogen test in mango

Nitrogen (N) is an essential nutrient in mango, influencing both productivity and fruit quality. In Australia, tree N is traditionally assessed once a year in the dormant pre-flowering stage by laboratory analysis of leaf N. This single assessment is insufficient to determine tree N status at all stages of the annual phenological cycle. Development of a field-based rapid N test would allow more frequent monitoring of tree N status and improved fertiliser management. This experiment examined the accuracy and useability of several devices used in other horticultural crops to rapidly assess mango leaf N in the field; the Konica Minolta 'SPAD-502 chlorophyll meter', Horiba 'Cardy Meter' and the Merck 'RQflex 10'. Regression and correlation analyses were used to determine the relationship between total leaf N and the measurements from the rapid test devices. The relationship between the chlorophyll index measured by the SPAD-502 meter and leaf N is highly significant at late fruit set (R 2=0.72, n=40) and post-harvest (R2=0.81, n=40) stages in the mango cultivar 'Kensington Pride' and significant (R2=0.51, n=40) at the flowering stage, indicating the device can be used to rapidly assess mango leaf N in the field. Correlation analysis indicated the relationship between petiole sap measured with the Cardy or Merck devices and leaf N is non-significant. © 2013 ISHS.

Rapid monitoring of nitrogen in mango trees

Nitrogen (N) is an essential nutrient in mango, influencing both productivity and fruit quality. In Australian mango orchards, tree N is traditionally assessed once a year at the dormant pre-flowering stage using laboratory analysis of leaf N. This single assessment is insufficient to determine tree N status at all stages of the annual phenological cycle. Development of a field-based rapid N test would allow more frequent monitoring of tree N status and improved fertiliser management. These experiments examined the accuracy and useability of several devices used in other horticultural crops to rapidly assess mango leaf N in the field; the Konica Minolta 'SPAD-502 chlorophyll meter', Horiba 'Cardy Meter' and the Merck 'RQflex 10.' Regression and correlation analyses were used to determine the relationship between total leaf N and the measurements from the rapid test devices. The relationship between the chlorophyll index measured by the SPAD-502 meter and leaf N was highly significant at late fruit set (R 2=0.72, n=40) and post-harvest (R 2=0.81, n=40) stages and significant at the flowering stage (R 2=0.51, n=40) in the cultivar 'Kensington Pride', indicating the device can be used to rapidly assess mango leaf N in the field. Correlation analysis indicated the relationship between petiole sap measured with the Cardy or Merck devices and leaf N was non-significant.

Correlation between NIRS generated and chemically measured feed quality data in barley (Hordeum vulgare), and potential use in QTL analysis identification

A study was performed to investigate the value of near infrared reflectance spectroscopy (NIRS) as an alternate method to analytical techniques for identifying QTL associated with feed quality traits. Milled samples from an F6-derived recombinant inbred Tallon/Scarlett population were incubated in the rumen of fistulated cattle, recovered, washed and dried to determine the in-situ dry matter digestibility (DMD). Both pre- and post-digestion samples were analysed using NIRS to quantify key quality components relating to acid detergent fibre, starch and protein. This phenotypic data was used to identify trait associated QTL and compare them to previously identified QTL. Though a number of genetic correlations were identified between the phenotypic data sets, the only correlation of most interest was between DMD and starch digested (r = -0.382). The significance of this genetic correlation was that the NIRS data set identified a putative QTL on chromosomes 7H (LOD = 3.3) associated with starch digested. A QTL for DMD occurred in the same region of chromosome 7H, with flanking markers fAG/CAT63 and bPb-0758. The significant correlation and identification of this putative QTL, highlights the potential of technologies like NIRS in QTL analysis.

High-throughput prediction of eucalypt lignin syringyl/guaiacyl content using multivariate analysis: a comparison between mid-infrared, near-infrared, and Raman spectroscopies for model development

BACKGROUND: In order to rapidly and efficiently screen potential biofuel feedstock candidates for quintessential traits, robust high-throughput analytical techniques must be developed and honed. The traditional methods of measuring lignin syringyl/guaiacyl (S/G) ratio can be laborious, involve hazardous reagents, and/or be destructive. Vibrational spectroscopy can furnish high-throughput instrumentation without the limitations of the traditional techniques. Spectral data from mid-infrared, near-infrared, and Raman spectroscopies was combined with S/G ratios, obtained using pyrolysis molecular beam mass spectrometry, from 245 different eucalypt and Acacia trees across 17 species. Iterations of spectral processing allowed the assembly of robust predictive models using partial least squares (PLS). RESULTS: The PLS models were rigorously evaluated using three different randomly generated calibration and validation sets for each spectral processing approach. Root mean standard errors of prediction for validation sets were lowest for models comprised of Raman (0.13 to 0.16) and mid-infrared (0.13 to 0.15) spectral data, while near-infrared spectroscopy led to more erroneous predictions (0.18 to 0.21). Correlation coefficients (r) for the validation sets followed a similar pattern: Raman (0.89 to 0.91), mid-infrared (0.87 to 0.91), and near-infrared (0.79 to 0.82). These statistics signify that Raman and mid-infrared spectroscopy led to the most accurate predictions of S/G ratio in a diverse consortium of feedstocks. CONCLUSION: Eucalypts present an attractive option for biofuel and biochemical production. Given the assortment of over 900 different species of Eucalyptus and Corymbia, in addition to various species of Acacia, it is necessary to isolate those possessing ideal biofuel traits. This research has demonstrated the validity of vibrational spectroscopy to efficiently partition different potential biofuel feedstocks according to lignin S/G ratio, significantly reducing experiment and analysis time and expense while providing non-destructive, accurate, global, predictive models encompassing a diverse array of feedstocks.

Statistical methods for analysis of multi-harvest data from perennial pasture variety selection trials

Variety selection in perennial pasture crops involves identifying best varieties from data collected from multiple harvest times in field trials. For accurate selection, the statistical methods for analysing such data need to account for the spatial and temporal correlation typically present. This paper provides an approach for analysing multi-harvest data from variety selection trials in which there may be a large number of harvest times. Methods are presented for modelling the variety by harvest effects while accounting for the spatial and temporal correlation between observations. These methods provide an improvement in model fit compared to separate analyses for each harvest, and provide insight into variety by harvest interactions. The approach is illustrated using two traits from a lucerne variety selection trial. The proposed method provides variety predictions allowing for the natural sources of variation and correlation in multi-harvest data.