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.

Abundance and mortality of overwintering pupae of Helicoverpa armigera (Hübner) (Lepidoptera: Noctuidae) on the Darling Downs, Queensland, Australia

Surveys were conducted between 1997 and 2001 to investigate the incidence of overwintering Helicoverpa spp. pupae under summer crop residues on the Darling Downs, Queensland. Only Helicoverpa armigera was represented in collections of overwintering pupae. The results indicated that late-season crops of cotton, sorghum, maize, soybean, mungbean and sunflower were equally likely to have overwintering pupae under them. In the absence of tillage practices, these crops had the potential to produce similar numbers of moths/ha in the spring. There were expected differences between years in the densities of overwintering pupae and the number of emerged moths/ha. Irrigated crops produced 2.5 times more moths/ha than dryland crops. Overall survival from autumn-formed pupae to emerged moths averaged 44%, with a higher proportion of pupae under maize surviving to produce moths than each of the other crops. Parasitoids killed 44.1% of pupae, with Heteropelma scaposum representing 83.3% of all parasitoids reared from pupae. Percentage parasitism levels were lower in irrigated crops (27.6%) compared with dryland crops (40.5%). Recent changes to Helicoverpa spp. management in cotton/grain-farming systems in south-eastern Queensland, including widespread adoption of Bt cotton, and use of more effective and more selective insecticides, could lead to lower densities of overwintering pupae under late summer crops.

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.

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

Kinetics of interaction of Cotton Leaf Curl Kokhran Virus-Dabawali (CLCuKV-Dab) coat protein and its mutants with ssDNA

Gemini viral assembly and transport of viral DNA into nucleus for replication, ssentially involve DNA-coat protein interactions. The kinetics of interaction of Cotton LeafCtirl Kokhran Virus-Dabawali recombinant coat protein (rCP) with DNA was studied by electrophoretic mobility shift assay (EMSA) and Surface plasmon resonance (SPR). The rCP interacted with ssDNA with a K-A, of 2.6 +/- 0.29 x 10(8) M-1 in a sequence non-specific manner. The CP has a conserved C2H2 type zinc finger motif composed of residues C68, C72, H81 and H85. Mutation of these residues to alanine resulted in reduced binding to DNA probes. The H85A mutant rCP showed the least binding with approximately 756 fold loss in the association rate and a three order magnitude decrease in the binding affinity as compared to rCP. The CP-DNA interactions via the zinc finger motif could play a Crucial role ill Virus assembly and in nuclear transport. (C) 2009 Elsevier Inc.

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.

How accurate are the marker orders in crop linkage maps generated from large marker datasets?

Marker ordering during linkage map construction is a critical component of QTL mapping research. In recent years, high-throughput genotyping methods have become widely used, and these methods may generate hundreds of markers for a single mapping population. This poses problems for linkage analysis software because the number of possible marker orders increases exponentially as the number of markers increases. In this paper, we tested the accuracy of linkage analyses on simulated recombinant inbred line data using the commonly used Map Manager QTX (Manly et al. 2001: Mammalian Genome 12, 930-932) software and RECORD (Van Os et al. 2005: Theoretical and Applied Genetics 112, 30-40). Accuracy was measured by calculating two scores: % correct marker positions, and a novel, weighted rank-based score derived from the sum of absolute values of true minus observed marker ranks divided by the total number of markers. The accuracy of maps generated using Map Manager QTX was considerably lower than those generated using RECORD. Differences in linkage maps were often observed when marker ordering was performed several times using the identical dataset. In order to test the effect of reducing marker numbers on the stability of marker order, we pruned marker datasets focusing on regions consisting of tightly linked clusters of markers, which included redundant markers. Marker pruning improved the accuracy and stability of linkage maps because a single unambiguous marker order was produced that was consistent across replications of analysis. Marker pruning was also applied to a real barley mapping population and QTL analysis was performed using different map versions produced by the different programs. While some QTLs were identified with both map versions, there were large differences in QTL mapping results. Differences included maximum LOD and R-2 values at QTL peaks and map positions, thus highlighting the importance of marker order for QTL mapping

Inter-seasonal population dynamics and pest status of Bemisia tabaci (Gennadius) biotype B in an Australian cropping system

Bemisia tabaci, biotype B, commonly known as the silverleaf whitefly (SLW) is an alien species that invaded Australia in the mid-90s. This paper reports on the invasion ecology of SLW and the factors that are likely to have contributed to the first outbreak of this major pest in an Australian cotton cropping system, population dynamics of SLW within whitefly-susceptible crop (cotton and cucurbit) and non-crop vegetation (sowthistle, Sonchus spp.) components of the cropping system were investigated over four consecutive growing seasons (September-June) 2001/02-2004/05 in the Emerald Irrigation Area (EIA) of Queensland, Australia. Based on fixed geo-referenced sampling sites, variation in spatial and temporal abundance of SLW within each system component was quantified to provide baseline data for the development of ecologically sustainable pest management strategies. Parasitism of large (3rd and 4th instars) SLW nymphs by native aphelinid wasps was quantified to determine the potential for natural control of SLW populations. Following the initial outbreak in 2001/02, SLW abundance declined and stabilised over the next three seasons. The population dynamics of SLW is characterised by inter-seasonal population cycling between the non-crop (weed) and cotton components of the EIA cropping system. Cotton was the largest sink for and source of SLW during the study period. Over-wintering populations dispersed from weed host plant sources to cotton in spring followed by a reverse dispersal in late summer and autumn to broad-leaved crops and weeds. A basic spatial source-sink analysis showed that SLW adult and nymph densities were higher in cotton fields that were closer to over-wintering weed sources throughout spring than in fields that were further away. Cucurbit fields were not significant sources of SLW and did not appear to contribute significantly to the regional population dynamics of the pest. Substantial parasitism of nymphal stages throughout the study period indicates that native parasitoid species and other natural enemies are important sources of SLW mortality in Australian cotton production systems. Weather conditions and use of broad-spectrum insecticides for pest control are implicated in the initial outbreak and on-going pest status of SLW in the region.

Germination, emergence, and persistence of Sonchus oleraceus, a major crop weed in subtropical Australia.

Sonchus oleraceus (common sowthistle) is a dominant weed and has increased in prevalence in conservation cropping systems of the subtropical grain region of Australia. Four experiments were undertaken to define the environmental factors that favor its germination, emergence, and seed persistence. Seeds were germinated at constant temperatures between 5 and 35C and water potentials between 0 and -1.4 MPa. The maximum germination rate of 86-100% occurred at 0 and -0.2 MPa, irrespective of the temperature when exposed to light (12 h photoperiod light/dark), but the germination rate was reduced by 72% without light. At water potentials of -0.6 to -0.8 MPa, the germination rate was reduced substantially by higher temperatures; no seed germinated at a water potential >-1.0 MPa. Emergence and seed persistence were measured over 30 months following seed burial at 0 (surface), 1, 2, 5, and 10 cm depths in large pots that were buried in a south-eastern Queensland field. Seedlings emerged readily from the surface and 1 cm depth, with no emergence from below the 2 cm depth. The seedlings emerged during any season following rain but, predominantly, within 6 months of planting. Seed persistence was short-term on the soil surface, with 2% of seeds remaining after 6 months, but it increased with the burial depth, with 12% remaining after 30 months at 10 cm. Thus, a minimal seed burial depth with reduced tillage and increased surface soil water with stubble retention has favored the proliferation of this weed in any season in a subtropical environment. However, diligent management without seed replenishment will greatly reduce this weed problem within a short period.

Gaseous Nitrogen Losses Following Soil Amendment with Biosolids under Controlled Conditions.

This paper quantifies gaseous N losses due to ammonia volatilisation and denitrification under controlled conditions at 30 degrees C and 75% to 150% of Field Capacity (FC). Biosolids were mixed with two contrasting soils from subtropical Australia at a rate designed to meet crop N requirements for irrigated cotton or maize (i.e., equivalent to 180 kg N ha(-1)). In the first experiment, aerobically (AE) and anaerobically (AN) digested biosolids were mixed into a heavy Vertosol soil and then incubated for 105 days. Ammonia volatilization over 72 days accounted for less than 4% of the applied NH4-N but 24% (AN) to 29% (AE) of the total applied biosolids' N was lost through denitrification in 105 days. In the second experiment AN biosolids with and without added polyacrimide polymer were mixed with either a heavy Vertosol or a lighter Red Ferrosol and then incubated for 98 days. The N loss was higher from the Vertosol with 16-29% of total N applied versus the Red Ferrosol with 7-10% of total N applied, while addition of polymer to the biosolids increased N loss from 7 to 10% and from 16 to 29% in the Red Ferrosol and Vertosol, respectively. A major product from the denitrification process was N-2 gas, accounting for >90% of the emitted N gases from both experiments. Our findings demonstrate that denitrification could be a major pathway of gaseous N losses under warm and moist conditions.

Rice husk biochar and crop residue amendment in subtropical cropping soils: Effect on biomass production, nitrogen use efficiency and greenhouse gas emissions

We investigated the effect of maize residues and rice husk biochar on biomass production, fertiliser nitrogen recovery (FNR) and nitrous oxide (N2O) emissions for three different subtropical cropping soils. Maize residues at two rates (0 and 10 t ha−1) combined with three rates (0, 15 and 30 t ha-1) of rice husk biochar were added to three soil types in a pot trial with maize plants. Soil N2O emissions were monitored with static chambers for 91 days. Isotopic 15N-labelled urea was applied to the treatments without added crop residues to measure the FNR. Crop residue incorporation significantly reduced N uptake in all treatments but did not affect overall FNR. Rice husk biochar amendment had no effect on plant growth and N uptake but significantly reduced N2O and carbon dioxide (CO2) emissions in two of the three soils. The incorporation of crop residues had a contrasting effect on soil N2O emissions depending on the mineral N status of the soil. The study shows that effects of crop residues depend on soil properties at the time of application. Adding crop residues with a high C/N ratio to soil can immobilise N in the soil profile and hence reduce N uptake and/or total biomass production. Crop residue incorporation can either stimulate or reduce N2O emissions depending on the mineral N content of the soil. Crop residues pyrolysed to biochar can potentially stabilise native soil C (negative priming) and reduce N2O emissions from cropping soils thus providing climate change mitigation potential beyond the biochar C storage in soils. Incorporation of crop residues as an approach to recycle organic materials and reduce synthetic N fertiliser use in agricultural production requires a thorough evaluation, both in terms of biomass production and greenhouse gas emissions.