Direct seeding of chenopod shrubs for saltland and rangeland environments

There are currently two ways of establishing chenopod shrubs: sowing from seed using a niche seeder, or planting nursery-raised seedlings with a tree planter. Planting seedlings is the more reliable method, but is relatively expensive (in excess of $450 per hectare). On the other hand, direct seeding using the specialised “niche seeder” is much less expensive ($100-150 per hectare), but is also less reliable. This project aimed to investigate alternative methods of direct seeding chenopod shrubs for saltland and rangeland areas by developing a greater understanding of their seed biology and agronomic requirements. Our aspiration was that shrubs should be established using more conventional farm machinery. This bulletin reports on a combination of seed biology and agronomic research to develop reliable, low-cost direct seeding options for chenopod shrubs. Experiments into the impact of changing environmental conditions on seeds were studied in the laboratory, and field experiments were conducted to test the applicability of these insights in the field using conventional modified farm seeding machinery. As a result of this work, a successful direct seeding package using farm seeding equipment (modified for wide row spacings and depth control) was developed for Atriplex nummularia (old man saltbush), the most widely planted saltbush species across southern Australia. The nine key elements of the package are: 1. Select suitable paddocks for introduction of new shrubs 2. Prepare a weed-free seedbed using two knockdown herbicide applications (4-6 weeks and 1-2 weeks before seeding) and commence control of rabbits and kangaroos 3. Sow the best seed, by ensuring: a. Large fruits, with a high proportion of viable seeds, have been selected b. Seed is of subspecies nummularia (not subsp. spathulata) c. Fruits have been harvested within the previous six months and stored in a cool, dry environment d. Bracts are retained around the seeds 4. Sow into moisture in late winter - early spring (depending on district) a. If the area to be sown is waterlogged, defer sowing until later in spring b. If insufficient soil moisture, defer sowing until the following year 5. Use a sowing rate of ~10 fruits/m (if germination rate is 15%) to provide at least one plant for every 2 m of row; use higher rates for seed of lower germination 6. Set the seeder up to sow into furrows with trailing press wheels 7. Sow to a depth of 5-10 mm (very critical) 8. Control weeds and pests (insects, mites, kangaroos and rabbits) 9. Defer grazing until seedlings are well established This establishment method has also been shown to work for Rhagodia preissii (mallee saltbush). This project was not able to develop reliable direct seeding packages for other Atriplex species, including A. amnicola and A. undulata. Further work is needed to understand the triggers for their germination, before these species can be direct-seeded with conventional machinery. Direct sowing of M. brevifolia and M. pyramidata appears to be problematic in much of southern Australia, due to their requirement for temperatures >30°C for germination, which do not occur within the normal winter growing season. An exception to this would be areas with more reliable summer rainfall, such as northern New South Wales, where sowing could be deferred until late spring-early summer. An alternative strategy for establishing M. brevifolia, is to encourage natural recruitment of seedlings from seed produced on surrounding bushes (if it is already present in the area), or to transplant a low density of nursery-raised seedlings, which could then act as a seed source for natural recruitment (if it is not already present)

Image labelling and the statistical analysis of incomplete data

No abstract available

Image labelling and the statistical analysis of incomplete data

No abstract available

A cautionary note about crossvalidatory choice

No abstract available

Noise estimation in signal restoration using regularisation

Several estimators of variance are compared in the context of problems where smoothing is incorporated in the estimation of regression functions. Many of the estimators are scaled quadratic forms in the data, and some incorporate steps that try to accommodate substantial nonconstancy in the response function. The estimators are described and an account is provided of an empirical study based on four one-dimensional source functions of varying degrees of irregularity

Asymptotically optimal difference-based estimation of variance in nonparametric regression

We define and compute asymptotically optimal difference sequences for estimating error variance in homoscedastic nonparametric regression. Our optimal difference sequences do not depend on unknowns, such as the mean function, and provide substantial improvements over the suboptimal sequences commonly used in practice. For example, in the case of normal data the usual variance estimator based on symmetric second-order differences is only 64% efficient relative to the estimator based on optimal second-order differences. The efficiency of an optimal mth-order difference estimator relative to the error sample variance is 2m/(2m + 1). Again this is for normal data, and increases as the tails of the error distribution become heavier

An empirical study of the simulation of various models used for images

Markov random fields are typically used as priors in Bayesian image restoration methods to represent spatial information in the image. Commonly used Markov random fields are not in fact capable of representing the moderate-to-large scale clustering present in naturally occurring images and can also be time consuming to simulate, requiring iterative algorithms which can take hundreds of thousands of sweeps of the image to converge. Markov mesh models, a causal subclass of Markov random fields, are, however, readily simulated. We describe an empirical study of simulated realizations from various models used in the literature, and we introduce some new mesh-type models. We conclude, however, that while large-scale clustering may be represented by such models, strong directional effects are also present for all but very limited parameterizations. It is emphasized that the results do not detract from the use of Markov random fields as representers of local spatial properties, which is their main purpose in the implementation of Bayesian statistical approaches to image analysis. Brief allusion is made to the issue of parameter estimation

On estimation of noise variance in two-dimensional signal processing

Estimation of noise variance is an important component of digital signal processing, in particular of image processing. In this paper we develop methods for estimating the variance of white noise in a two-dimensional degraded signal. We discuss optimal configurations of pixels for difference-based estimation, and describe asymptotically optimal selection of weights for the component pixels. After extensive analysis of possible configurations we recommend averaging linear configurations over a variety of different orientations (usually two or four). This approach produces estimators with properties of both statistical and numerical efficiency

On the estimation of noisy binary Markov random fields

Possolo (Technical Report No. 77, Department of Statistics GN-22, University of Washington, Seattle (1986)) and Derin and Elliott (IEEE Trans. Pattern Analysis Mach. Intell.PAMI-9, 39–55 (1987)) proposed, for the estimation of binary and more general m-ary Markov random fields, the “logit” method, based on histogramming an image. The authors applied the method to noise-free Markov random fields. For estimation of noisy images, one might recursively implement the logit method within an iterative restoration algorithm, such as the iterated conditional modes (ICM) method of Besag (J. R. Statist. Soc. B48, 259–302 (1986)), by alternating parameter estimation and restoration. It is noted that failure to smooth zero histogram counts, for the purposes of estimation, can cause ICM to cycle indefinitely

On estimation of noise variance in two-dimensional signal processing

Estimation of noise variance is an important component of digital signal processing, in particular of image processing. In this paper we develop methods for estimating the variance of white noise in a two-dimensional degraded signal. We discuss optimal configurations of pixels for difference-based estimation, and describe asymptotically optimal selection of weights for the component pixels. After extensive analysis of possible configurations we recommend averaging linear configurations over a variety of different orientations (usually two or four). This approach produces estimators with properties of both statistical and numerical efficiency