Effect of crop establishment methods and weed control treatments on weed management, and rice yield

Lower water availability coupled with labor shortage has resulted in the increasing inability of growers to cultivate puddled transplanted rice (PTR). A field study was conducted in the wet season of 2012 and dry season of 2013 to evaluate the performance of five rice establishment methods and four weed control treatments on weed management, and rice yield. Grass weeds were higher in dry-seeded rice (DSR) as compared to PTR and nonpuddled transplanted rice (NPTR). The highest total weed density (225-256plantsm-2) and total weed biomass (315-501gm-2) were recorded in DSR while the lowest (102-129plantsm-2 and 75-387gm-2) in PTR. Compared with the weedy plots, the treatment pretilachlor followed by fenoxaprop plus ethoxysulfuron plus 2,4-D provided excellent weed control. This treatment, however, had a poor performance in NPTR. In both seasons, herbicide efficacy was better in DSR and wet-seeded rice. PTR and DSR produced the maximum rice grain yields. The weed-free plots and herbicide treatments produced 84-614% and 58-504% higher rice grain yield, respectively, than the weedy plots in 2012, and a similar trend was observed in 2013.

Integrating pest population models with biophysical crop models to better represent the farming system

Farming systems frameworks such as the Agricultural Production Systems simulator (APSIM) represent fluxes through the soil, plant and atmosphere of the system well, but do not generally consider the biotic constraints that function within the system. We designed a method that allowed population models built in DYMEX to interact with APSIM. The simulator engine component of the DYMEX population-modelling platform was wrapped within an APSIM module allowing it to get and set variable values in other APSIM models running in the simulation. A rust model developed in DYMEX is used to demonstrate how the developing rust population reduces the crop's green leaf area. The success of the linking process is seen in the interaction of the two models and how changes in rust population on the crop's leaves feedback to the APSIM crop modifying the growth and development of the crop's leaf area. This linking of population models to simulate pest populations and biophysical models to simulate crop growth and development increases the complexity of the simulation, but provides a tool to investigate biotic constraints within farming systems and further moves APSIM towards being an agro-ecological framework.

Gene flow from transgenic plant populations: models and applications for risk assessment

The future use of genetically modified (GM) plants in food, feed and biomass production requires a careful consideration of possible risks related to the unintended spread of trangenes into new habitats. This may occur via introgression of the transgene to conventional genotypes, due to cross-pollination, and via the invasion of GM plants to new habitats. Assessment of possible environmental impacts of GM plants requires estimation of the level of gene flow from a GM population. Furthermore, management measures for reducing gene flow from GM populations are needed in order to prevent possible unwanted effects of transgenes on ecosystems. This work develops modeling tools for estimating gene flow from GM plant populations in boreal environments and for investigating the mechanisms of the gene flow process. To describe spatial dimensions of the gene flow, dispersal models are developed for the local and regional scale spread of pollen grains and seeds, with special emphasis on wind dispersal. This study provides tools for describing cross-pollination between GM and conventional populations and for estimating the levels of transgenic contamination of the conventional crops. For perennial populations, a modeling framework describing the dynamics of plants and genotypes is developed, in order to estimate the gene flow process over a sequence of years. The dispersal of airborne pollen and seeds cannot be easily controlled, and small amounts of these particles are likely to disperse over long distances. Wind dispersal processes are highly stochastic due to variation in atmospheric conditions, so that there may be considerable variation between individual dispersal patterns. This, in turn, is reflected to the large amount of variation in annual levels of cross-pollination between GM and conventional populations. Even though land-use practices have effects on the average levels of cross-pollination between GM and conventional fields, the level of transgenic contamination of a conventional crop remains highly stochastic. The demographic effects of a transgene have impacts on the establishment of trangenic plants amongst conventional genotypes of the same species. If the transgene gives a plant a considerable fitness advantage in comparison to conventional genotypes, the spread of transgenes to conventional population can be strongly increased. In such cases, dominance of the transgene considerably increases gene flow from GM to conventional populations, due to the enhanced fitness of heterozygous hybrids. The fitness of GM plants in conventional populations can be reduced by linking the selectively favoured primary transgene to a disfavoured mitigation transgene. Recombination between these transgenes is a major risk related to this technique, especially because it tends to take place amongst the conventional genotypes and thus promotes the establishment of invasive transgenic plants in conventional populations.

Plant composition modulates arthropod pest and predator abundance: Evidence for culling exotics and planting natives

We investigate the role of plant species in crops, pasture and native vegetation remnants in supporting agronomic pests and their predators. The study was conducted in three Australian States and across 290 sites sampled monthly for two years. Pastures played a key role in harbouring pest species consistent across States, while native vegetation hosted relatively more predators than other habitat types within each State. Furthermore, native plant species supported the lowest pest density and more predators than pests; in contrast, 75 of the exotic weed species surveyed hosted more pests than predators. Despite the role of pasture in harbouring pests, we found in NSW that pasture also supported the highest proportion of juvenile predators, while native vegetation remnants had the lowest. Our results indicate that non-crop habitat (native remnants or pasture) with few exotic weeds supports high predator and low pest arthropod densities, and that weeds are associated with high pest densities. By linking broad response variables such as ‘all pests’ with specific predictors such as ‘plant species’, our study will inform on-farm management actions of which weeds to control and which natives to plant or regenerate. This study shows the importance of knowing the function of habitats and plants species in supporting pests and predators in agricultural landscapes across multiple regions.

Physiological basis of yield variation in response to row spacing and plant density of mungbean grown in subtropical environments

In this study, we investigated the extent and physiological bases of yield variation due to row spacing and plant density configuration in the mungbean Vigna radiata (L.) Wilczek variety “Crystal” grown in different subtropical environments. Field trials were conducted in six production environments; one rain-fed and one irrigated trial each at Biloela and Emerald, and one rain-fed trial each at Hermitage and Kingaroy sites in Queensland, Australia. In each trial, six combinations of spatial arrangement of plants, achieved through two inter-row spacings of 1 m or 0.9 m (wide row), 0.5 m or 0.3 m (narrow row), with three plant densities, 20, 30 and 40 plants/m2, were compared. The narrow row spacing resulted in 22% higher shoot dry matter and 14% more yield compared to the wide rows. The yield advantage of narrow rows ranged from 10% to 36% in the two irrigated and three rain-fed trials. However, yield loss of up to 10% was also recorded from narrow rows at Emerald where the crop suffered severe drought. Neither the effects of plant density, nor the interaction between plant density and row spacing, however, were significant in any trial. The yield advantage of narrow rows was related to 22% more intercepted radiation. In addition, simulations by the Agricultural Production Systems Simulator model, using site-specific agronomy, soil and weather information, suggested that narrow rows had proportionately greater use of soil water through transpiration, compared to evaporation resulting in higher yield per mm of soil water. The long-term simulation of yield probabilities over 123 years for the two row configurations showed that the mungbean crop planted in narrow rows could produce up to 30% higher grain yield compared to wide rows in 95% of the seasons.

Soybean rotation and crop residue management to reduce nitrous oxide emissions from sugarcane soils

NITROUS OXIDE (N2O) IS a potent greenhouse gas and the predominant ozone-depleting substance in the atmosphere. Agricultural nitrogenous fertiliser use is the major source of human-induced N2O emissions. A field experiment was conducted at Bundaberg from October 2012 to September 2014 to examine the impacts of legume crop (soybean) rotation as an alternative nitrogen (N) source on N2O emissions during the fallow period and to investigate low-emission soybean residue management practices. An automatic monitoring system and manual gas sampling chambers were used to measure greenhouse gas emissions from soil. Soybean cropping during the fallow period reduced N2O emissions compared to the bare fallow. Based on the N content in the soybean crop residues, the fertiliser N application rate was reduced by about 120 kg N/ha for the subsequent sugarcane crop. Consequently, emissions of N2O during the sugarcane cropping season were significantly lower from the soybean cropped soil than those from the conventionally fertilised (145 kg N/ha) soil following bare fallow. However, tillage that incorporated the soybean crop residues into soil promoted N2O emissions in the first two months. Spraying a nitrification inhibitor (DMPP) onto the soybean crop residues before tillage effectively prevented the N2O emission spikes. Compared to conventional tillage, practising no-till with or without growing a nitrogen catch crop during the time after soybean harvest and before cane planting also reduced N2O emissions substantially. These results demonstrated that soybean rotation during the fallow period followed with N conservation management practices could offer a promising N2O mitigation strategy in sugarcane farming. Further investigation is required to provide guidance on N and water management following soybean fallow to maintain sugar productivity.

Effect of added nitrogen on plant litter decomposition depends on initial soil carbon and nitrogen stoichiometry

Increasing organic carbon inputs to agricultural soils through the use of pastures or crop residues has been suggested as a means of restoring soil organic carbon lost via anthropogenic activities, such as land use change. However, the decomposition and retention of different plant residues in soil, and how these processes are affected by soil properties and nitrogen fertiliser application, is not fully understood. We evaluated the rate and extent of decomposition of 13C-pulse labelled plant material in response to nitrogen addition in four pasture soils of varying physico-chemical characteristics. Microbial respiration of buffel grass (Cenchrus ciliaris L.), wheat (Triticum aestivum L.) and lucerne (Medicago sativa L.) residues was monitored over 365-days. A double exponential model fitted to the data suggested that microbial respiration occurred as an early rapid and a late slow stage. A weighted three-compartment mixing model estimated the decomposition of both soluble and insoluble plant 13C (mg C kg−1 soil). Total plant material decomposition followed the alkyl C: O-alkyl C ratio of plant material, as determined by solid-state 13C nuclear magnetic resonance spectroscopy. Urea-N addition increased the decomposition of insoluble plant 13C in some soils (≤0.1% total nitrogen) but not others (0.3% total nitrogen). Principal components regression analysis indicated that 26% of the variability of plant material decomposition was explained by soil physico-chemical characteristics (P = 0.001), which was primarily described by the C:N ratio. We conclude that plant species with increasing alkyl C: O-alkyl C ratio are better retained as soil organic matter, and that the C:N stoichiometry of soils determines whether N addition leads to increases in soil organic carbon stocks.

Molecular Breeding for Complex Adaptive Traits: How Integrating Crop Ecophysiology and Modelling Can Enhance Efficiency

Progress in crop improvement is limited by the ability to identify favourable combinations of genotypes (G) and management practices (M) in relevant target environments (E) given the resources available to search among the myriad of possible combinations. To underpin yield advance we require prediction of phenotype based on genotype. In plant breeding, traditional phenotypic selection methods have involved measuring phenotypic performance of large segregating populations in multi-environment trials and applying rigorous statistical procedures based on quantitative genetic theory to identify superior individuals. Recent developments in the ability to inexpensively and densely map/sequence genomes have facilitated a shift from the level of the individual (genotype) to the level of the genomic region. Molecular breeding strategies using genome wide prediction and genomic selection approaches have developed rapidly. However, their applicability to complex traits remains constrained by gene-gene and gene-environment interactions, which restrict the predictive power of associations of genomic regions with phenotypic responses. Here it is argued that crop ecophysiology and functional whole plant modelling can provide an effective link between molecular and organism scales and enhance molecular breeding by adding value to genetic prediction approaches. A physiological framework that facilitates dissection and modelling of complex traits can inform phenotyping methods for marker/gene detection and underpin prediction of likely phenotypic consequences of trait and genetic variation in target environments. This approach holds considerable promise for more effectively linking genotype to phenotype for complex adaptive traits. Specific examples focused on drought adaptation are presented to highlight the concepts.

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.

The role of MicroRNAs in stress response in the resurrection plant Tripogon loliiformis

Research in this thesis focussed on the improvement of agricultural crops in increasing water use efficiency that impacts global crop productivity. The study identified key genetic regulatory mechanisms that the resurrection plant Tripogon loliiformis utilises to tolerate desiccation. Due to the conserved nature of the pathways involved, this information can be transferred for the enhancement of drought tolerance and water use efficiency in agricultural crops. Specifically this study used high throughput sequencing, microscopy and plant transformation to further the understanding of post-transcriptional regulatory mechanisms. It was shown that T. loliiformis uses microRNAs to regulate pro-survival autophagy pathways to tolerate desiccation.

Trehalose accumulation triggers autophagy during plant desiccation

Global climate change, increasingly erratic weather and a burgeoning global population are significant threats to the sustainability of future crop production. There is an urgent need for the development of robust measures that enable crops to withstand the uncertainty of climate change whilst still producing maximum yields. Resurrection plants possess the unique ability to withstand desiccation for prolonged periods, can be restored upon watering and represent great potential for the development of stress tolerant crops. Here, we describe the remarkable stress characteristics of Tripogon loliiformis, an uncharacterised resurrection grass and close relative of the economically important cereals, rice, sorghum, and maize. We show that T. loliiformis survives extreme environmental stress by implementing autophagy to prevent Programmed Cell Death. Notably, we identified a novel role for trehalose in the regulation of autophagy in T.loliiformis. Transcriptome, Gas Chromatography Mass Spectrometry, immunoblotting and confocal microscopy analyses directly linked the accumulation of trehalose with the onset of autophagy in dehydrating and desiccated T. loliiformis shoots. These results were supported in vitro with the observation of autophagosomes in trehalose treated T. loliiformis leaves; autophagosomes were not detected in untreated samples. Presumably, once induced, autophagy promotes desiccation tolerance in T.loliiformis , by removal of cellular toxins to suppress programmed cell death and the recycling of nutrients to delay the onset of senescence. These findings illustrate how resurrection plants manipulate sugar metabolism to promote desiccation tolerance and may provide candidate genes that are potentially useful for the development of stress tolerant crops.

Assessment of crop insect damage using unmanned aerial systems: A machine learning approach

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 technology 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 the approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labeling 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. The outcome of this approach is a soft K-means algorithm similar to the EM algorithm for Gaussian mixture models. The results show the algorithm delivers decision boundaries that consistently classify the field into three clusters, one for each crop health level. The methodology presented in this paper represents a venue for further research towards automated crop damage assessments and biosecurity surveillance.

Context-dependency of a complex fruit-frugivore mutualism: temporal variation in crop size and neighborhood effects

The quantity of fruit consumed by dispersers is highly variable among individuals within plant populations. The outcome Of Such selection operated by firugivores has been examined mostly with respect to changing spatial contexts. The influence of varying temporal contexts on frugivore choice, and their possible demographic and evolutionary consequences is poorly understood. We examined if temporal variation in fruit availability across a hierarchy of nested temporal levels (interannual, intraseasonal, 120 h, 24 h) altered frugivore choice for a complex seed dispersal system in dry tropical forests of southern India. The interactions between Phyllanthus emblica and its primary disperser (ruminants) was mediated by another frugivore (a primate),which made large quantities of fruit available on the ground to ruminants. The direction and strength of crop size and neighborhood effects on this interaction varied with changing temporal contexts.Fruit availability was higher in the first of the two study years, and at the start of the season in both years. Fruit persistence on trees,determined by primate foraging, was influenced by crop size andconspecific neighborhood densities only in the high fruit availability year. Fruit removal by ruminants was influenced by crop size in both years and neighborhood densities only in the high availability year. In both years, these effects were stronger at the start of the season.Intraseasonal reduction in fruit availability diminished inequalities in fruit removal by ruminants and the influence of crop size and fruiting neighborhoods. All trees were not equally attractive to frugivores in a P. emblica population at all points of time. Temporal asymmetry in frugivore-mediated selection could reduce potential for co-evolution between firugivores and plants by diluting selective pressures. Inter-dependencies; formed between disparate animal consumers can add additional levels of complexity to plant-frugivore mutualistic networks and have potential reproductive consequences for specific individuals within populations.

Ecology of the Asian Elephant in Southern India. II. Feeding Habits and Crop Raiding Patterns

The Asian elephant's foraging strategy in its natural habitat and in cultivation was studied in southern India during 1981-83. Though elephants consumed at least 112 plant species in the study area, about 85% of their diet consisted of only 25 species from the order Malvales and the families Leguminosae, Palmae, Cyperaceae and Gramineae. Alteration between a predominantly browse diet during the dry season with a grass diet during the early wet season was related to the seasonally changing protein content of grasses. Crop raiding, which was sporadic during the dry season, gradually increased with more area being cultivated with the onset of rains. Raiding frequency reached a peak during October-December, with some villages being raided almost every night, when finger millet (Eleusine coracana) was cultivated by most farmers. The monthly frequency of raiding was related to the seasonal movement of elephant herds and to the size of the enclave. Of their total annual food requirement, adult bull elephants derived an estimated 9.3% and family herds 1.7% in quantity from cultivated land. Cultivated cereal and millet crops provided significantly more protein, calcium and sodium than the wild grasses. Ultimately, crop raiding can be thought of as an extension of the elephant's optimal foraging strategy.

Studies on the submerged plant community in Lake Biwa. I. Distribution and productivity of Egeria densa. A submerged plant invader in the south basin [Translation from: Physiol.Ecol.Japan 17, 283-290, 1976]

Egeria densa (PLANCH.) ST. JOHN, a submerged plant invader, forms a wide submerged plant zone, particularly along the west coast of the south basin, Lake Biwa. The macrophyte occupies over 82% of the plant zone in the basin and its biomass reaches 93% of the total. The estimated annual net production was approximately 1 kg dry wt./m2 in a dense area, which is about 4.5 times as much as the net production by phytoplankton in an offshore area of the basin. Although the area covered by the macrophyte is only 5.8% of the total of the basin, it produced about one-tenth of the total annual primary production. In the most productive season Egeria produced 46% of the total primary productivity. Thus, the macrophyte never be neglected when one considers the energy flow or material circulation in the basin. This study was initiated in order to clarify the role of submerged macrophytes, particularly E. densa, in Lake Biwa. The following points are reported in this paper: the distribution of macrophytes in the south basin; seasonal change in standing crop of E. densa; seasonal change in values related to production, utilizing a model proposed by Ikushima with its parameters experimentally determined.