Beyond chemical dependency for managing plant-parasitic nematodes: Examples from the banana, pineapple and vegetable industries of tropical and subtropical Australia

Plant-parasitic nematodes are important pests of horticultural crops grown in tropical and subtropical regions of Australia. Burrowing nematode (Radopholus similis) is a major impediment to banana production and root-knot nematodes (predominantly Meloidogyne javanica and M. incognita) cause problems on pineapple and a range of annual vegetables, including tomato, capsicum, zucchini, watermelon, rockmelon, potato and sweet potato. In the early 1990s, nematode control in these industries was largely achieved with chemicals, with methyl bromide widely used on some subtropical vegetable crops, ethylene dibromide applied routinely to pineapples and non-volatile nematicides such as fenamiphos applied up to four times a year in banana plantations. This paper discusses the research and extension work done over the last 15 years to introduce an integrated pest management approach to nematode control in tropical and subtropical horticulture. It then discusses various components of current integrated pest management programs, including crop rotation, nematode monitoring, clean planting material, organic amendments, farming systems to enhance biological suppression of nematodes and judicious use of nematicides. Finally, options for improving current management practices are considered.

Crop and environmental attributes underpinning genotype by environment interaction in synthetic-derived bread wheat evaluated in Mexico and Australia

Synthetic backcrossed-derived bread wheats (SBWs) from CIMMYT were grown in the north-west of Mexico (CIANO) and sites across Australia during 3 seasons. A different set of lines was evaluated each season, as new materials became available from the CIMMYT crop enhancement program. Previously, we have evaluated both the performance of genotypes across environments and the genotype x environment interaction (G x E). The objective of this study was to interpret the G x E for yield in terms of crop attributes measured at individual sites and to identify the potential environmental drivers of this interaction. Groups of SBWs with consistent yield performance were identified, often comprising closely related lines. However, contrasting performance was also relatively common among sister lines or between a recurrent parent and its SBWs. Early flowering was a common feature among lines with broad adaptation and/or high yield in the northern Australian wheatbelt, while yields in the southern region did not show any association with the maturity type. Lines with high yields in the southern and northern regions had cooler canopies during flowering and early grain filling. Among the SBWs with Australian genetic backgrounds, lines best adapted to CIANO were tall (>100 cm), with a slightly higher ground cover. These lines also displayed a higher concentration of water-soluble carbohydrates in the stem at flowering, which was negatively correlated with stem number per unit area when evaluated in southern Australia (Horsham). Possible reasons for these patterns are discussed. Selection for yield at CIANO did not specifically identify the lines best adapted to northern Australia, although they were not the most poorly adapted either. In addition, groups of lines with specific adaptation to the south would not have been selected by choosing the highest yielding lines at CIANO. These findings suggest that selection at CIMMYT for Australian environments may be improved by either trait based selection or yield data combined with trait information. Flowering date, canopy temperature around flowering, tiller density, and water-soluble carbohydrate concentration in the stem at flowering seem likely candidates.

High subsoil chloride concentrations reduce soil water extraction and crop yield on Vertosols in north-eastern Australia

Salinity, sodicity, acidity, and phytotoxic levels of chloride (Cl) in subsoils are major constraints to crop production in many soils of north-eastern Australia because they reduce the ability of crop roots to extract water and nutrients from the soil. The complex interactions and correlations among soil properties result in multi-colinearity between soil properties and crop yield that makes it difficult to determine which constraint is the major limitation. We used ridge-regression analysis to overcome colinearity to evaluate the contribution of soil factors and water supply to the variation in the yields of 5 winter crops on soils with various levels and combinations of subsoil constraints in the region. Subsoil constraints measured were soil Cl, electrical conductivity of the saturation extract (ECse), and exchangeable sodium percentage (ESP). The ridge regression procedure selected several of the variables used in a descriptive model, which included in-crop rainfall, plant-available soil water at sowing in the 0.90-1.10 m soil layer, and soil Cl in the 0.90-1.10 m soil layer, and accounted for 77-85% of the variation in the grain yields of the 5 winter crops. Inclusion of ESP of the top soil (0.0-0.10 m soil layer) marginally increased the descriptive capability of the models for bread wheat, barley and durum wheat. Subsoil Cl concentration was found to be an effective substitute for subsoil water extraction. The estimates of the critical levels of subsoil Cl for a 10% reduction in the grain yield were 492 mg cl/kg for chickpea, 662 mg Cl/kg for durum wheat, 854 mg Cl/kg for bread wheat, 980 mg Cl/kg for canola, and 1012 mg Cl/kg for barley, thus suggesting that chickpea and durum wheat were more sensitive to subsoil Cl than bread wheat, barley, and canola.

Analysis of high yielding maize production - A study based on a commercial crop

This paper reports on the use of APSIM - Maize for retrospective analysis of performance of a high input, high yielding maize crop and analysis of predicted performance of maize grown with high inputs over the long-term (>100 years) for specified scenarios of environmental conditions (temperature and radiation) and agronomic inputs (sowing date, plant population, nitrogen fertiliser and irrigation) at Boort, Victoria, Australia. It uses a high yielding (17 400 kg/ha dry grain, 20 500 kg/ha at 15% water) commercial crop grown in 2004-05 as the basis of the study. Yield for the agronomic and environmental conditions of 2004-05 was predicted accurately, giving confidence that the model could be used for the detailed analyses undertaken. The analysis showed that the yield achieved was close to that possible with the conditions and agronomic inputs of 2004-05. Sowing dates during 21 September to 26 October had little effect on predicted yield, except when combined with reduced temperature. Single year and long-term analyses concluded that a higher plant population (11 plants/m2) is needed to optimise yield, but that slightly lower N and irrigation inputs are appropriate for the plant population used commercially (8.4 plants/m2). Also, compared with changes in agronomic inputs increases in temperature and/or radiation had relatively minor effects, except that reduced temperature reduces predicted yield substantially. This study provides an approach for the use of models for both retrospective analysis of crop performance and assessment of long-term variability of crop yield under a wide range of agronomic and environmental conditions.

Subsoil constraints in Vertosols: Crop water use nutrient concentration, and grain yields of bread wheat, durum wheat, barley, chickpea and canola

Single or multiple factors implicated in subsoil constraints including salinity, sodicity, and phytotoxic concentrations of chloride (Cl) are present in many Vertosols including those occurring in Queensland, Australia. The variable distribution and the complex interactions that exist between these constraints limit the agronomic or management options available to manage the soil with these subsoil constraints. The identification of crops and cultivars adapted to these adverse subsoil conditions and/or able to exploit subsoil water may be an option to maintain productivity of these soils. We evaluated relative performance of 5 winter crop species, in terms of grain yields, nutrient concentration, and ability to extract soil water, grown on soils with various levels and combinations of subsoil constraints in 19 field experiments over 2 years. Subsoil constraints were measured by levels of soil Cl, electrical conductivity of the saturation extract (ECse), and exchangeable sodium percentage (ESP). Increasing levels of subsoil constraints significantly decreased maximum depth of water extraction, grain yield, and plant-available water capacity for all the 5 crops and more so for chickpea and durum wheat than bread wheat, barley, or canola. Increasing soil Cl levels had a greater restricting effect on water availability than did ECse and ESP. We developed empirical relationships between soil Cl, ECse, and ESP and crop lower limit (CLL) for estimating subsoil water extraction by 5 winter crops. However, the presence of gypsum influenced the ability to predict CLL based on the levels of ECse. Stronger relationships between apparent unused plant-available water (CLL - LL15; LL15 is lower limit at -1.5 MPa) and soil Cl concentrations than ESP or ECse suggested that the presence of high Cl in these soils most likely inhibited the subsoil water extraction by the crops. This was supported by increased sodium (Na) and Cl concentration with a corresponding decrease in calcium (Ca) and potassium (K) in young mature leaf of bread wheat, durum wheat, and chickpea with increasing levels of subsoil constraints. Of the 2 ions, Na and Cl, the latter appears to be more damaging than the former, resulting in plant dieback and reduced grain yields.

Seagrass-Watch: Engaging Torres Strait Islanders in marine habitat monitoring

Involvement in scientifically structured habitat monitoring is a relatively new concept to the peoples of Torres Strait. The approach we used was to focus on awareness, and to build the capacity of groups to participate using Seagrass-Watch as the vehicle to provide education and training in monitoring marine ecosystems. The project successfully delivered quality scientifically rigorous baseline information on the seasonality of seagrasses in the Torres Strait-a first for this region. Eight seagrass species were identified across the monitoring sites. Seagrass cover varied within and between years. Preliminary evidence indicated that drivers for seagrass variability were climate related. Generally, seagrass abundance increased during the north-west monsoon (Kuki), possibly a consequence of elevated nutrients, lower tidal exposure times, less wind, and higher air temperatures. Low seagrass abundance coincided with the presence of greater winds and longer periods of exposure at low tides during the south-east trade wind season (Sager). No seasonal patterns were apparent when frequency of disturbance from high sedimentation and human impacts was high. Seagrass-Watch has been incorporated in to the Thursday Island High School's Marine Studies Unit ensuring continuity of monitoring. The students, teachers, and other interested individuals involved in Seagrass-Watch have mastered the necessary scientific procedures to monitor seagrass meadows, and developed skills in coordinating a monitoring program and skills in mentoring younger students. This has increased the participants' self-esteem and confidence, and given them an insight into how they may participate in the future management of their sea country.

Detection of nitrogen deficiency in wheat from spectral reflectance indices and basic crop eco-physiological concepts

We tested the capacity of several published multispectral indices to estimate the nitrogen nutrition of wheat canopies grown under different levels of water supply and plant density and derived a simple canopy reflectance index that is greatly independent of those factors. Planar domain geometry was used to account for mixed signals from the canopy and soil when the ground cover was low. A nitrogen stress index was developed, which adjusts shoot %N for plant biomass and area, thereby accounting for environmental conditions that affect growth, such as crop water status. The canopy chlorophyll content index (CCCi) and the modified spectral ratio planar index (mSRPi) could explain 68 and 69% of the observed variability in the nitrogen nutrition of the crop as early as Zadoks 33, irrespective of water status or ground cover. The CCCi was derived from the combination of 3 wavebands 670, 720 and 790 nm, and the mSRPi from 445, 705 and 750 nm, together with broader bands in the NIR and RED. The potential for their spatial application over large fields/paddocks is discussed.

Long-term response of corn to limited irrigation and crop rotations

Dwindling water supplies for irrigation are prompting alternative management choices by irrigators. Limited irrigation, where less water is applied than full crop demand, may be a viable approach. Application of limited irrigation to corn was examined in this research. Corn was grown in crop rotations with dryland, limited irrigation, or full irrigation management from 1985 to 1999. Crop rotations included corn following corn (continuous corn), corn following wheat, followed by soybean (wheat-corn-soybean), and corn following soybean (corn-soybean). Full irrigation was managed to meet crop evapotranspiration requirements (ETc). Limited irrigation was managed with a seasonal target of no more than 150 mm applied. Precipitation patterns influenced the outcomes of measured parameters. Dryland yields had the most variation, while fully irrigated yields varied the least. Limited irrigation yields were 80% to 90%> of fully irrigated yields, but the limited irrigation plots received about half the applied water. Grain yields were significantly different among irrigation treatments. Yields were not significantly different among rotation treatments for all years and water treatments. For soil water parameters, more statistical differences were detected among the water management treatments than among the crop rotation treatments. Economic projections of these management practices showed that full irrigation produced the most income if water was available. Limited irrigation increased income significantly from dryland management.

Soil properties and crop yields in a dryland Vertisol sown with cotton-based crop rotations

Information on the effects of growing cotton (Gossypium hirsutum L.)-based crop rotations on soil quality of dryland Vertisols is sparse. The objective of this study was to quantify the effects of growing cereal and leguminous crops in rotation with dryland cotton on physical and chemical properties of a grey Vertisol near Warra, SE Queensland, Australia. The experimental treatments, selected after consultations with local cotton growers, were continuous cotton (T1), cotton-sorghum (Sorghum bicolor (L.) Moench.) (T2), cotton-wheat (Triticum aestivum L.) double cropped (T3), cotton-chickpea (Cicer arietinum L.) double cropped followed by wheat (T4) and cotton-wheat (T5). From 1993 to 1996 land preparation was by chisel ploughing to about 0.2 m followed by two to four cultivations with a Gyral tyne cultivator. Thereafter all crops were sown with zero tillage except for cultivation with a chisel plough to about 0.07-0.1 m after cotton picking to control heliothis moth pupae. Soil was sampled from 1996 to 2004 and physical (air-filled porosity of oven-dried soil, an indicator of soil compaction; plastic limit; linear shrinkage; dispersion index) and chemical (pH in 0.01 M CaCl2, organic carbon, exchangeable Ca, Mg, K and Na contents) properties measured. Crop rotation affected soil properties only with respect to exchangeable Na content and air-filled porosity. In the surface 0.15 m during 2000 and 2001 lowest air-filled porosity occurred with T1 (average of 34.6 m3/100 m3) and the highest with T3 (average of 38.9 m3/100 m3). Air-filled porosity decreased in the same depth between 1997 and 1998 from 45.0 to 36.1 m3/100 m3, presumably due to smearing and compaction caused by shallow cultivation in wet soil. In the subsoil, T1 and T2 frequently had lower air-filled porosity values in comparison with T3, T4 and T5, particularly during the early stages of the experiment, although values under T1 increased subsequently. In general, compaction was less under rotations which included a wheat crop (T3, T4, T5). For example, average air-filled porosity (in m3/100 m3) in the 0.15-0.30 m depth from 1996 to 1999 was 19.8 with both T1 and T2, and 21.2 with T3, 21.1 with T4 and 21.5 with T5. From 2000 to 2004, average air-filled porosity (in m3/100 m3) in the same depth was 21.3 with T1, 19.0 with T2, 19.8 with T3, 20.0 with T4 and 20.5 with T5. The rotation which included chickpea (T4) resulted in the lowest exchangeable Na content, although differences among rotations were small. Where only a cereal crop with a fibrous root system was sown in rotation with cotton (T2, T3, T5) linear shrinkage in the 0.45-0.60 m depth was lower than in rotations, which included tap-rooted crops such as chickpea (T4) or continuous cotton (T1). Dispersion index and organic carbon decreased, and plastic limit increased with time. Soil organic carbon stocks decreased at a rate of 1.2 Mg/ha/year. Lowest average cotton lint yield occurred with T2 (0.54 Mg/ha) and highest wheat yield with T3 (2.8 Mg/ha). Rotations which include a wheat crop are more likely to result in better soil structure and cotton lint yield than cotton-sorghum or continuous cotton.

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.

Annual forecasting of the Australian macadamia crop - integrating tree census data with statistical climate-adjustment models

To facilitate marketing and export, the Australian macadamia industry requires accurate crop forecasts. Each year, two levels of crop predictions are produced for this industry. The first is an overall longer-term forecast based on tree census data of growers in the Australian Macadamia Society (AMS). This data set currently accounts for around 70% of total production, and is supplemented by our best estimates of non-AMS orchards. Given these total tree numbers, average yields per tree are needed to complete the long-term forecasts. Yields from regional variety trials were initially used, but were found to be consistently higher than the average yields that growers were obtaining. Hence, a statistical model was developed using growers' historical yields, also taken from the AMS database. This model accounted for the effects of tree age, variety, year, region and tree spacing, and explained 65% of the total variation in the yield per tree data. The second level of crop prediction is an annual climate adjustment of these overall long-term estimates, taking into account the expected effects on production of the previous year's climate. This adjustment is based on relative historical yields, measured as the percentage deviance between expected and actual production. The dominant climatic variables are observed temperature, evaporation, solar radiation and modelled water stress. Initially, a number of alternate statistical models showed good agreement within the historical data, with jack-knife cross-validation R2 values of 96% or better. However, forecasts varied quite widely between these alternate models. Exploratory multivariate analyses and nearest-neighbour methods were used to investigate these differences. For 2001-2003, the overall forecasts were in the right direction (when compared with the long-term expected values), but were over-estimates. In 2004 the forecast was well under the observed production, and in 2005 the revised models produced a forecast within 5.1% of the actual production. Over the first five years of forecasting, the absolute deviance for the climate-adjustment models averaged 10.1%, just outside the targeted objective of 10%.

Laboratory information management software for genotyping workflows: Applications in high throughput crop genotyping

Background: With the advances in DNA sequencer-based technologies, it has become possible to automate several steps of the genotyping process leading to increased throughput. To efficiently handle the large amounts of genotypic data generated and help with quality control, there is a strong need for a software system that can help with the tracking of samples and capture and management of data at different steps of the process. Such systems, while serving to manage the workflow precisely, also encourage good laboratory practice by standardizing protocols, recording and annotating data from every step of the workflow Results: A laboratory information management system (LIMS) has been designed and implemented at the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) that meets the requirements of a moderately high throughput molecular genotyping facility. The application is designed as modules and is simple to learn and use. The application leads the user through each step of the process from starting an experiment to the storing of output data from the genotype detection step with auto-binning of alleles; thus ensuring that every DNA sample is handled in an identical manner and all the necessary data are captured. The application keeps track of DNA samples and generated data. Data entry into the system is through the use of forms for file uploads. The LIMS provides functions to trace back to the electrophoresis gel files or sample source for any genotypic data and for repeating experiments. The LIMS is being presently used for the capture of high throughput SSR (simple-sequence repeat) genotyping data from the legume (chickpea, groundnut and pigeonpea) and cereal (sorghum and millets) crops of importance in the semi-arid tropics. Conclusions: A laboratory information management system is available that has been found useful in the management of microsatellite genotype data in a moderately high throughput genotyping laboratory. The application with source code is freely available for academic users and can be downloaded from http://www.icrisat.org/bt-software-d-lims.htm

Rapid internal drainage rates in Ferrosols

Adoption of conservation tillage practices on Red Ferrosol soils in the inland Burnett area of south-east Queensland has been shown to reduce runoff and subsequent soil erosion. However, improved infiltration resulting from these measures has not improved crop performance and there are suggestions of increased loss of soil water via deep drainage. This paper reports data monitoring soil water under real and artificial rainfall events in commercial fields and long-term tillage experiments, and uses the data to explore the rate and mechanisms of deep drainage in this soil type. Soils were characterised by large drainable porosities (≥0.10 m3/m3) in all parts of the profile to depths of 1.50 m, with drainable porosity similar to available water content (AWC) at 0.25 and 0.75 m, but >60% higher than AWC at 1.50 m. Hydraulic conductivity immediately below the tilled layer in both continuously cropped soils and those after a ley pasture phase was shown to decline with increasing soil moisture content, although the rate of decline was much greater in continuously cropped soil. At moisture contents approaching the drained upper limit (pore water pressure = -100cm H2O), estimates of saturated hydraulic conductivity after a ley pasture were 3-5 times greater than in continuously cropped soil, suggesting much greater rates of deep drainage in the former when soils are moist. Hydraulic tensiometers and fringe capacitance sensors monitored during real and artificial rainfall events showed evidence of soils approaching saturation in the surface layers (top 0.30-0.40 m), but there was no evidence of soil moistures exceeding the drained upper limit (i.e. pore water pressures ≤ -100 cm H2O) in deeper layers. Recovery of applied soil water within the top 1.00-1.20 m of the profile during or immediately after rainfall events declined as the starting profile moisture content increased. These effects were consistent with very rapid rates of internal drainage. Sensors deeper in the profile were unable to detect this drainage due to either non-uniformity of conducting macropores (i.e. bypass flow) or unsaturated conductivities in deeper layers that far exceed the saturated hydraulic conductivity of the infiltration throttle at the bottom of the cultivated layer. Large increases in unsaturated hydraulic conductivities are likely with only small increases in water content above the drained upper limit. Further studies with drainage lysimeters and large banks of hydraulic tensiometers are planned to quantify drainage risk in these soil types.

Estimating crop area using seasonal time series of enhanced vegetation index from MODIS satellite imagery

Cereal grain is one of the main export commodities of Australian agriculture. Over the past decade, crop yield forecasts for wheat and sorghum have shown appreciable utility for industry planning at shire, state, and national scales. There is now an increasing drive from industry for more accurate and cost-effective crop production forecasts. In order to generate production estimates, accurate crop area estimates are needed by the end of the cropping season. Multivariate methods for analysing remotely sensed Enhanced Vegetation Index (EVI) from 16-day Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery within the cropping period (i.e. April-November) were investigated to estimate crop area for wheat, barley, chickpea, and total winter cropped area for a case study region in NE Australia. Each pixel classification method was trained on ground truth data collected from the study region. Three approaches to pixel classification were examined: (i) cluster analysis of trajectories of EVI values from consecutive multi-date imagery during the crop growth period; (ii) harmonic analysis of the time series (HANTS) of the EVI values; and (iii) principal component analysis (PCA) of the time series of EVI values. Images classified using these three approaches were compared with each other, and with a classification based on the single MODIS image taken at peak EVI. Imagery for the 2003 and 2004 seasons was used to assess the ability of the methods to determine wheat, barley, chickpea, and total cropped area estimates. The accuracy at pixel scale was determined by the percent correct classification metric by contrasting all pixel scale samples with independent pixel observations. At a shire level, aggregated total crop area estimates were compared with surveyed estimates. All multi-temporal methods showed significant overall capability to estimate total winter crop area. There was high accuracy at pixel scale (>98% correct classification) for identifying overall winter cropping. However, discrimination among crops was less accurate. Although the use of single-date EVI data produced high accuracy for estimates of wheat area at shire scale, the result contradicted the poor pixel-scale accuracy associated with this approach, due to fortuitous compensating errors. Further studies are needed to extrapolate the multi-temporal approaches to other geographical areas and to improve the lead time for deriving cropped-area estimates before harvest.

Non-specific conducting polymer-based array capable of monitoring odour emissions from a biofiltration system in a piggery building

A commercial non-specific gas sensor array system was evaluated in terms of its capability to monitor the odour abatement performance of a biofiltration system developed for treating emissions from a commercial piggery building. The biofiltration system was a modular system comprising an inlet ducting system, humidifier and closed-bed biofilter. It also included a gravimetric moisture monitoring and water application system for precise control of moisture content of an organic woodchip medium. Principal component analysis (PCA) of the sensor array measurements indicated that the biofilter outlet air was significantly different to both inlet air of the system and post-humidifier air. Data pre-processing techniques including normalising and outlier handling were applied to improve the odour discrimination performance of the non-specific gas sensor array. To develop an odour quantification model using the sensor array responses of the non-specific sensor array, PCA regression, artificial neural network (ANN) and partial least squares (PLS) modelling techniques were applied. The correlation coefficient (r(2)) values of the PCA, ANN, and PLS models were 0.44, 0.62 and 0.79, respectively.