Allelopathy for weed control in agricultural systems

Weeds are a hidden foe for crop plants, interfering with their functions and suppressing their growth and development. Yield losses of ∼34 are caused by weeds among the major crops, which are grown worldwide. These yield losses are higher than the losses caused by other pests in the crops. Sustainable weed management is needed in the wake of a huge decline in crop outputs due to weed pressure. A diversity in weed management tools ensures sustainable weed control and reduces chances of herbicide resistance development in weeds. Allelopathy as a tool, can be importantly used to combat the challenges of environmental pollution and herbicide resistance development. This review article provides a recent update regarding the practical application of allelopathy for weed control in agricultural systems. Several studies elaborate on the significance of allelopathy for weed management. Rye, sorghum, rice, sunflower, rape seed, and wheat have been documented as important allelopathic crops. These crops express their allelopathic potential by releasing allelochemicals which not only suppress weeds, but also promote underground microbial activities. Crop cultivars with allelopathic potentials can be grown to suppress weeds under field conditions. Further, several types of allelopathic plants can be intercropped with other crops to smother weeds. The use of allelopathic cover crops and mulches can reduce weed pressure in field crops. Rotating a routine crop with an allelopathic crop for one season is another method of allelopathic weed control. Importantly, plant breeding can be explored to improve the allelopathic potential of crop cultivars. In conclusion, allelopathy can be utilized for suppressing weeds in field crops. Allelopathy has a pertinent significance for ecological, sustainable, and integrated weed management systems.

Queensland Agricultural Journal Volume 13 Parts 1-6 July to December 1903

General Index Vol 13 Parts 1-6, pages 1-81

Application of sorbers to mitigate greenhouse gas emissions from land-applied pig litter

Nitrous oxide is the foremost greenhouse gas (GHG)generated by land-applied manures and chemical fertilisers (Australian Government 2013). This research project was part of the National Agricultural Manure Management Program and investigated the potential for sorbers (i.e. specific naturally-occurring minerals) to decrease GHG emissions from spent piggery litter (as well as other manures)applied to soils. The sorbers investigated in this research were vermiculite and bentonite. Both are clays with high cation exchange capacities, of approximately 100–150 cmol/kg Faure 1998). The hypothesis tested in this study was that the sorbers bind ammonium in soil solution thereby suppressing ammonia (NH3)volatilisation and in doing so, slowing the kinetics of nitrate formation and associated nitrous oxide (N2O) emissions. A series of laboratory, glasshouse and field experiments were conducted to assess the sorbers’ effectiveness. The laboratory experiments comprised 64 vessels containing manure and sorber/manure ratios ranging from 1 : 10 to 1 : 1 incorporated into a sandy Sodosol via mixing. The glasshouse trial involved 240 pots comprising manure/sorber incubations placed 5 cm below the soil surface, two soil types (sandy Sodosol and Ferrosol) and two different nitrogen (N) application rates (50 kg N/ha and 150 kg N/ha) with a model plant (kikuyu grass). The field trial consisted of 96, 2 m · 2 m plots on a Ferrosol site with digit grass used as a model plant. Manure/ sorber mixtures were applied in trenches (5 cm below surface) to these plots at increasing sorber levels at anNloading rate of 200 kg/ha. Gas produced in all experiments was plumbed into a purpose-built automated gas analysis (N2O, NH3, CH4, CO2) system. In the laboratory experiments, the sorbers showed strong capacity to decreaseNH3 emissions (up to 80% decrease). Ammonia emissions were close to the detection limit in all treatments in the glasshouse and field trial. In all experiments, considerable N2O decreases (>40%) were achieved by the sorbers. As an example, mean N2O emission decreases from the field trial phase of the project are shown in Fig. 1a. The decrease inGHGemissions brought about by the clays did not negatively impact agronomic performance. Both vermiculite and bentonite resulted in a significant increase in dry matter yields in the field trial (Fig. 1b). Continuing work will optimise the sorber technology for improved environmental and agronomic performance across a range of soils (Vertosol, Dermosol in addition to Ferrosol and Sodosols) and environmental parameters (moisture, temperature, porosity, pH).

Consumption of crops by feral pigs (Sus scrofa) in a fragmented agricultural landscape

Feral pigs (Sus scrofa) consume and damage crops and impact the environment through predation, competition and habitat disturbance, although supporting dietary data are lacking in agricultural landscapes. This study was undertaken to determine the relative importance of food items in the diet of feral pigs in a fragmented agricultural landscape, particularly to assist in predicting the breadth of likely impacts. Diet composition was assessed from the stomach contents of 196 feral pigs from agricultural properties in southern Queensland. Feral pigs were herbivorous, with plant matter comprising >99% of biomass consumed. Crops were consumed more frequently than non-crop species, and comprised >60% of dietary biomass, indicating a clear potential for direct economic losses. Consumption of pasture and forage species also suggests potential competition for pasture with domestic stock. There is little evidence of direct predation on native fauna, but feral pig feeding activities may impact environmental values. Seasonal differences in consumption of crop, pasture or animal food groups probably reflect the changing availability of food items. We recommend that future dietary studies examine food availability to determine any dietary preferences to assist in determining the foods most susceptible to damage. The outcomes of this study are important for developing techniques for monitoring the impacts of feral pigs, essential for developing management options to reduce feral pig damage on agricultural lands.

Fauna community trends during early restoration of alluvial open forest/woodland ecosystems on former agricultural land

Vertebrate fauna was studied over 10 years following revegetation of a Eucalyptus tereticornis ecosystem on former agricultural land. We compared four vegetation types: remnant forest, plantings of a mix of native tree species on cleared land, natural regeneration of partially cleared land after livestock removal, and cleared pasture land with scattered paddock trees managed for livestock production. Pasture differed significantly from remnant in both bird and nonbird fauna. Although 10 years of ecosystem restoration is relatively short term in the restoration process, in this time bird assemblages in plantings and natural regeneration had diverged significantly from pasture, but still differed significantly from remnant. After 10 years, 70 and 66% of the total vertebrate species found in remnant had been recorded in plantings and natural regeneration, respectively. Although the fauna assemblages within plantings and natural regeneration were tracking toward those of remnant, significant differences in fauna between plantings and natural regeneration indicated community development along different restoration pathways. Because natural regeneration contained more mature trees (dbh > 30 cm), native shrub species, and coarse woody debris than plantings from the beginning of the study, these features possibly encouraged different fauna to the revegetation areas from the outset. The ability of plantings and natural regeneration to transition to the remnant state will be governed by a number of factors that were significant in the analyses, including shrub cover, herbaceous biomass, tree hollows, time since fire, and landscape condition. Both active and passive restoration produced significant change from the cleared state in the short term.

Managing reforestation to sequester carbon, increase biodiversity potential and minimize loss of agricultural land

Reforestation will have important consequences for the global challenges of mitigating climate change, arresting habitat decline and ensuring food security. We examined field-scale trade-offs between carbon sequestration of tree plantings and biodiversity potential and loss of agricultural land. Extensive surveys of reforestation across temperate and tropical Australia (N = 1491 plantings) were used to determine how planting width and species mix affect carbon sequestration during early development (< 15 year). Carbon accumulation per area increased significantly with decreasing planting width and with increasing proportion of eucalypts (the predominant over-storey genus). Highest biodiversity potential was achieved through block plantings (width > 40 m) with about 25% of planted individuals being eucalypts. Carbon and biodiversity goals were balanced in mixed-species plantings by establishing narrow belts (width < 20 m) with a high proportion (>75%) of eucalypts, and in monocultures of mallee eucalypt plantings by using the widest belts (ca. 6–20 m). Impacts on agriculture were minimized by planting narrow belts (ca. 4 m) of mallee eucalypt monocultures, which had the highest carbon sequestering efficiency. A plausible scenario where only 5% of highly-cleared areas (<30% native vegetation cover remaining) of temperate Australia are reforested showed substantial mitigation potential. Total carbon sequestration after 15 years was up to 25 Mt CO2-e year−1 when carbon and biodiversity goals were balanced and 13 Mt CO2-e year−1 if block plantings of highest biodiversity potential were established. Even when reforestation was restricted to marginal agricultural land (<$2000 ha−1 land value, 28% of the land under agriculture in Australia), total mitigation potential after 15 years was 17–26 Mt CO2-e year−1 using narrow belts of mallee plantings. This work provides guidance on land use to governments and planners. We show that the multiple benefits of young tree plantings can be balanced by manipulating planting width and species choice at establishment. In highly-cleared areas, such plantings can sequester substantial biomass carbon while improving biodiversity and causing negligible loss of agricultural land.

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.