High frequency fire alters C:N:P stoichiometry in forest litter

Fire is a major driver of ecosystem change and can disproportionately affect the cycling of different nutrients. Thus, a stoichiometric approach to investigate the relationships between nutrient availability and microbial resource use during decomposition is likely to provide insight into the effects of fire on ecosystem functioning. We conducted a field litter bag experiment to investigate the long-term impact of repeated fire on the stoichiometry of leaf litter C, N and P pools, and nutrient-acquiring enzyme activities during decomposition in a wet sclerophyll eucalypt forest in Queensland, Australia. Fire frequency treatments have been maintained since 1972, including burning every two years (2yrB), burning every four years (4yrB) and no burning (NB). C:N ratios in freshly fallen litter were 29-42% higher and C:P ratios were 6-25% lower for 2yrB than NB during decomposition, with correspondingly lower 2yrB N:P ratios (27-32) than for NB (34-49). Trends in litter soluble and microbial N:P ratios were similar to the overall litter N:P ratios across fire treatments. Consistent with these, the ratio of activities for N-acquiring to P-acquiring enzymes in litter was higher for 2yrB than NB while 4yrB was generally intermediate between 2yrB and NB. Decomposition rates of freshly fallen litter were significantly lower for 2yrB (72±2% mass remaining at the end of experiment) than for 4yrB (59±3%) and NB (62±3%), a difference that may be related to effects of N limitation, lower moisture content, and/or litter C quality. Results for older mixed-age litter were similar to those for freshly fallen litter although treatment differences were less pronounced. Overall, these findings show that frequent fire (2yrB) decoupled N and P cycling, as manifested in litter C:N:P stoichiometry and in microbial biomass N:P ratio and enzymatic activities. These data indicate that fire induced a transient shift to N-limited ecosystem conditions during the post-fire recovery phase. This article is protected by copyright. All rights reserved.

The introduction of transgenes to control blackheart in pineapple (Ananas Comosus L.) cv. Smooth Cayenne by microprojectile bombardment

A transformation technique for the introduction of transgenes to control blackheart by particle bombardment has been developed for pineapple cv. Smooth Cayenne. Leaf callus cultures capable of high frequency organogenesis with a short regeneration time were used as explant material. Gus and gfp reporter genes were used to observe and determine transient and stable expression. The ppo gene, isolated from pineapple, was introduced to control blackheart. Co-transformation occurred with constructs containing the nptII gene conferring geneticin resistance. We have recovered 15 independent transgenic gus and gfp lines each from 8 separate experiments and 22 ppo lines from 11 experiments. Gus, gfp, ppo and nptII positive plants have been regenerated, which have been shown by Southern blot analysis to be stable transgenics containing multiple copies of the introduced genes. These results show that biolistic gene delivery in pineapple can be successfully achieved at an acceptable efficiency of 0.21-1.5% for genetic improvement of 'Smooth Cayenne', the industry standard throughout the world.

Managing glyphosate resistance in Australian cotton farming: modelling shows how to delay evolution and maintain long-term population control

Glyphosate resistance is a rapidly developing threat to profitability in Australian cotton farming. Resistance causes an immediate reduction in the effectiveness of in-crop weed control in glyphosate-resistant transgenic cotton and summer fallows. Although strategies for delaying glyphosate resistance and those for managing resistant populations are qualitatively similar, the longer resistance can be delayed, the longer cotton growers will have choice over which tactics to apply and when to apply them. Effective strategies to avoid, delay, and manage resistance are thus of substantial value. We used a model of glyphosate resistance dynamics to perform simulations of resistance evolution in Sonchus oleraceus (common sowthistle) and Echinochloa colona (awnless barnyard grass) under a range of resistance prevention, delaying, and management strategies. From these simulations, we identified several elements that could contribute to effective glyphosate resistance prevention and management strategies. (i) Controlling glyphosate survivors is the most robust approach to delaying or preventing resistance. High-efficacy, high-frequency survivor control almost doubled the useful lifespan of glyphosate from 13 to 25 years even with glyphosate alone used in summer fallows. (ii) Two non-glyphosate tactics in-crop plus two in-summer fallows is the minimum intervention required for long-term delays in resistance evolution. (iii) Pre-emergence herbicides are important, but should be backed up with non-glyphosate knockdowns and strategic tillage; replacing a late-season, pre-emergence herbicide with inter-row tillage was predicted to delay glyphosate resistance by 4 years in awnless barnyard grass. (iv) Weed species' ecological characteristics, particularly seed bank dynamics, have an impact on the effectiveness of resistance strategies; S. oleraceus, because of its propensity to emerge year-round, was less exposed to selection with glyphosate than E. colona, resulting in an extra 5 years of glyphosate usefulness (18 v. 13 years) even in the most rapid cases of resistance evolution. Delaying tactics are thus available that can provide some or many years of continued glyphosate efficacy. If glyphosate-resistant cotton cropping is to remain profitable in Australian farming systems in the long-term, however, growers must adapt to the probability that they will have to deal with summer weeds that are no longer susceptible to glyphosate. Robust resistance management systems will need to include a diversity of weed control options, used appropriately.

Early recovery signs of an Australian grassland following the management of Parthenium hysterophorus L

Parthenium weed (Parthenium hysterophorus L.) is believed to reduce the above- and below-ground plant species diversity and the above-ground productivity in several ecosystems. We quantified the impact of this invasive weed upon species diversity in an Australian grassland and assessed the resulting shifts in plant community composition following management using two traditional approaches. A baseline plant community survey, prior to management, showed that the above-ground community was dominated by P. hysterophorus, stoloniferous grasses, with a further high frequency of species from Malvaceae, Chenopodiaceae and Amaranthaceae. In heavily invaded areas, P. hysterophorus abundance and biomass was found to negatively correlate with species diversity and native species abundance. Digitaria didactyla Willd. was present in high abundance when P. hysterophorus was not, with these two species, contributing most to the dissimilarity seen between areas. The application of selective broad leaf weed herbicides significantly reduced P. hysterophorus biomass under ungrazed conditions, but this management did not yet result in an increase in species diversity. In the above-ground community, P. hysterophorus was partly replaced by the introduced grass species Cynodon dactylon L. (Pers.) 1 year after management began, increasing the above-ground forage biomass production, while D. didactyla replaced P. hysterophorus in the below-ground community. This improvement in forage availability continued to strengthen over the time of the study resulting in a total increase of 80% after 2 years in the ungrazed treatment, demonstrating the stress that grazing was imposing upon this grassland-based agro-ecosystem and showing that it is necessary to remove grazing to obtain the best results from the chemical management approach.

Using UHF proximity loggers to quantify male–female interactions: A scoping study of estrous activity in cattle

Reproductive efficiency is an important determinant of profitable cattle breeding systems and the success of assisted reproductive techniques (ART) in wildlife conservation programs. Methods of estrous detection used in intensive beef and dairy cattle systems lack accuracy and remain the single biggest issue for improvement of reproductive rates and such methods are not practical for either large-scale extensive beef cattle enterprises or free-living mammalian species. Recent developments in UHF (ultra high frequency) proximity logger telemetry devices have been used to provide a continuous pair-wise measure of associations between individual animals for both livestock and wildlife. The objective of this study was to explore the potential of using UHF telemetry to identify the reproductive cycle phenotype in terms of intensity and duration of estrus. The study was conducted using Belmont Red (interbred Africander Brahman Hereford–Shorthorn) cattle grazing irrigated pasture on Belmont Research Station, northeastern Australia. The cow-bull associations from three groups of cows each with one bull were recorded over a 7-week breeding season and the stage of estrus was identified using ultrasonography. Telemetry data from bull and cows, collected over 4 8-day logger deployments, were log transformed and analyzed by ANOVA. Both the number and duration of bull-cow affiliations were significantly (P < 0.001) greater in estrous cows compared to anestrus cows. These results support the development of the UHF technology as a hands-off and noninvasive means of gathering socio-sexual information on both wildlife and livestock for reproductive management.

Diagnostic Molecular Markers for Phosphine Resistance in U.S. Populations of Tribolium castaneum and Rhyzopertha dominica

Stored product beetles that are resistant to the fumigant pesticide phosphine (hydrogen phosphide) gas have been reported for more than 40 years in many places worldwide. Traditionally, determination of phosphine resistance in stored product beetles is based on a discriminating dose bioassay that can take up to two weeks to evaluate. We developed a diagnostic cleaved amplified polymorphic sequence method, CAPS, to detect individuals with alleles for strong resistance to phosphine in populations of the red flour beetle, Tribolium castaneum, and the lesser grain borer, Rhyzopertha dominica, according to a single nucleotide mutation in the dihydrolipoamide dehydrogenase (DLD) gene. We initially isolated and sequenced the DLD genes from susceptible and strongly resistant populations of both species. The corresponding amino acid sequences were then deduced. A single amino acid mutation in DLD in populations of T.castaneum and R.dominica with strong resistance was identified as P45S in T.castaneum and P49S in R.dominica, both collected from northern Oklahoma, USA. PCR products containing these mutations were digested by the restriction enzymes MboI and BstNI, which revealed presence or absence, respectively of the resistant (R) allele and allowed inference of genotypes with that allele. Seven populations of T.castaneum from Kansas were subjected to discriminating dose bioassays for the weak and strong resistance phenotypes. Application of CAPS to these seven populations confirmed the R allele was in high frequency in the strongly resistant populations, and was absent or at a lower frequency in populations with weak resistance, which suggests that these populations with a low frequency of the R allele have the potential for selection of the strong resistance phenotype. CAPS markers for strong phosphine resistance will help to detect and confirm resistant beetles and can facilitate resistance management actions against a given pest population.

Legumes or nitrification inhibitors to reduce N2O emissions from subtropical cereal cropping systems in Oxisols?

The DAYCENT biogeochemical model was used to investigate how the use of fertilizers coated with nitrification inhibitors and the introduction of legumes in the crop rotation can affect subtropical cereal production and N2O emissions. The model was validated using comprehensive multi-seasonal, high-frequency dataset from two field investigations conducted on an Oxisol, which is the most common soil type in subtropical regions. Different N fertilizer rates were tested for each N management strategy and simulated under varying weather conditions. DAYCENT was able to reliably predict soil N dynamics, seasonal N2O emissions and crop production, although some discrepancies were observed in the treatments with low or no added N inputs and in the simulation of daily N2O fluxes. Simulations highlighted that the high clay content and the relatively low C levels of the Oxisol analyzed in this study limit the chances for significant amounts of N to be lost via deep leaching or denitrification. The application of urea coated with a nitrification inhibitor was the most effective strategy to minimize N2O emissions. This strategy however did not increase yields since the nitrification inhibitor did not substantially decrease overall N losses compared to conventional urea. Simulations indicated that replacing part of crop N requirements with N mineralized by legume residues is the most effective strategy to reduce N2O emissions and support cereal productivity. The results of this study show that legumes have significant potential to enhance the sustainable and profitable intensification of subtropical cereal cropping systems in Oxisols.

Automating Crop Damage Assessments with Unmanned Aerial Systems and Machine Learning

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising methodology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of this approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labelling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means clustering. The results show the algorithm delivers consistent decision boundaries that classify the field into three clusters, one for each crop health level as shown in Figure 1. The methodology presented in this paper represents a venue for further esearch towards automated crop damage assessments and biosecurity surveillance.