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.

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

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

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.

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.

Detection of Polymyxa graminis in a barley crop in Australia.

Polymyxa graminis was detected in the roots of barley plants from a field near Wondai, Queensland, in 2009. P. graminis was identified by characteristic sporosori in roots stained with trypan blue. The presence of P. graminis f. sp. tepida (which is hosted by wheat and oats as well as barley) in the roots was confirmed by specific PCR tests based on nuclear ribosomal DNA. P. graminis is the vector of several damaging soil-borne virus diseases of cereals in the genera Furovirus, Bymovirus and Pecluvirus. No virus particles were detected in sap extracts from leaves of stunted barley plants with leaf chlorosis and increased tillering. Further work is required to determine the distribution of P. graminis in Australian grain crops and the potential for establishment and spread of the exotic soil-borne viruses that it vectors.

Impact of tillage and residues from rotation crops on the nematode community in soil and surface mulch during the following sugarcane crop.

The impact of three cropping histories (sugarcane, maize and soybean) and two tillage practices (conventional tillage and direct drill) on plant-parasitic and free-living nematodes in the following sugarcane crop was examined in a field trial at Bundaberg. Soybean reduced populations of lesion nematode (Pratylenchus zeae) and root-knot nematode (Meloidogyne javanica) in comparison to previous crops of sugarcane or maize but increased populations of spiral nematode (Helicotylenchus dihystera) and maintained populations of dagger nematode (Xiphinema elongatum). However the effect of soybean on P zeae and M. javanica was no longer apparent 15 weeks after planting sugarcane, while later in the season, populations of these nematodes following soybean were as high as or higher than maize or sugarcane. Populations of P zeae were initially reduced by cultivation but due to strong resurgence tended to be higher in conventionally tilled than direct drill plots at the end of the plant crop. Even greater tillage effects were observed with M. javanica and X. elongatum, as nematode populations were significantly higher in conventionally tilled than direct drill plots late in the season. Populations of free-living nematodes in the upper 10 cm of soil were initially highest following soybean, but after 15, 35 and 59 weeks were lower than after sugarcane and contained fewer omnivorous and predatory nematodes. Conventional tillage increased populations of free-living nematodes in soil in comparison to direct drill and was also detrimental to omnivorous and predatory nematodes. These results suggest that crop rotation and tillage not only affect plant-parasitic nematodes directly, but also have indirect effects by impacting on natural enemies that regulate nematode populations. More than 2 million nematodes/m(2) were often present in crop residues on the surface of direct drill plots. Bacterial-feeding nematodes were predominant in residues early in the decomposition process but fungal-feeding nematodes predominated after 15 weeks. This indicates that fungi become an increasingly important component of the detritus food web as decomposition proceeds, and that that the rate of nutrient cycling decreases with time. Correlations between total numbers of free-living nematodes and mineral N concentrations in crop residues and surface soil suggested that the free-living nematode community may provide an indication of the rate of mineralisation of N from organic matter.

Better Macadamia crop forecasting

Better Macadamia crop forecasting.