Developmental and growth controls of tillering and water-soluble carbohydrate accumulation in contrasting wheat (Triticum aestivum L.) genotypes: can we dissect them?

In wheat, tillering and water-soluble carbohydrates (WSCs) in the stem are potential traits for adaptation to different environments and are of interest as targets for selective breeding. This study investigated the observation that a high stem WSC concentration (WSCc) is often related to low tillering. The proposition tested was that stem WSC accumulation is plant density dependent and could be an emergent property of tillering, whether driven by genotype or by environment. A small subset of recombinant inbred lines (RILs) contrasting for tillering was grown at different plant densities or on different sowing dates in multiple field experiments. Both tillering and WSCc were highly influenced by the environment, with a smaller, distinct genotypic component; the genotypeenvironment range covered 350750 stems m(2) and 25210mg g(1) WSCc. Stem WSCc was inversely related to stem number m(2), but genotypic rankings for stem WSCc persisted when RILs were compared at similar stem density. Low tilleringhigh WSCc RILs had similar leaf area index, larger individual leaves, and stems with larger internode cross-section and wall area when compared with high tilleringlow WSCc RILs. The maximum number of stems per plant was positively associated with growth and relative growth rate per plant, tillering rate and duration, and also, in some treatments, with leaf appearance rate and final leaf number. A common threshold of the red:far red ratio (0.390.44; standard error of the difference0.055) coincided with the maximum stem number per plant across genotypes and plant densities, and could be effectively used in crop simulation modelling as a ocut-off' rule for tillering. The relationship between tillering, WSCc, and their component traits, as well as the possible implications for crop simulation and breeding, is discussed.

Sustainable management for rangelands in a variable climate: evidence and insights from northern Australia

Inter-annual rainfall variability is a major challenge to sustainable and productive grazing management on rangelands. In Australia, rainfall variability is particularly pronounced and failure to manage appropriately leads to major economic loss and environmental degradation. Recommended strategies to manage sustainably include stocking at long-term carrying capacity (LTCC) or varying stock numbers with forage availability. These strategies are conceptually simple but difficult to implement, given the scale and spatial heterogeneity of grazing properties and the uncertainty of the climate. This paper presents learnings and insights from northern Australia gained from research and modelling on managing for rainfall variability. A method to objectively estimate LTCC in large, heterogeneous paddocks is discussed, and guidelines and tools to tactically adjust stocking rates are presented. The possible use of seasonal climate forecasts (SCF) in management is also considered. Results from a 13-year grazing trial in Queensland show that constant stocking at LTCC was far more profitable and largely maintained land condition compared with heavy stocking (HSR). Variable stocking (VAR) with or without the use of SCF was marginally more profitable, but income variability was greater and land condition poorer than constant stocking at LTCC. Two commercial scale trials in the Northern Territory with breeder cows highlighted the practical difficulties of variable stocking and provided evidence that heavier pasture utilisation rates depress reproductive performance. Simulation modelling across a range of regions in northern Australia also showed a decline in resource condition and profitability under heavy stocking rates. Modelling further suggested that the relative value of variable v. constant stocking depends on stocking rate and land condition. Importantly, variable stocking may possibly allow slightly higher stocking rates without pasture degradation. Enterprise-level simulations run for breeder herds nevertheless show that poor economic performance can occur under constant stocking and even under variable stocking in some circumstances. Modelling and research results both suggest that a form of constrained flexible stocking should be applied to manage for climate variability. Active adaptive management and research will be required as future climate changes make managing for rainfall variability increasingly challenging.

Stochastic model for simulating maize yield

Maize is one of the most important crops in the world. The products generated from this crop are largely used in the starch industry, the animal and human nutrition sector, and biomass energy production and refineries. For these reasons, there is much interest in figuring the potential grain yield of maize genotypes in relation to the environment in which they will be grown, as the productivity directly affects agribusiness or farm profitability. Questions like these can be investigated with ecophysiological crop models, which can be organized according to different philosophies and structures. The main objective of this work is to conceptualize a stochastic model for predicting maize grain yield and productivity under different conditions of water supply while considering the uncertainties of daily climate data. Therefore, one focus is to explain the model construction in detail, and the other is to present some results in light of the philosophy adopted. A deterministic model was built as the basis for the stochastic model. The former performed well in terms of the curve shape of the above-ground dry matter over time as well as the grain yield under full and moderate water deficit conditions. Through the use of a triangular distribution for the harvest index and a bivariate normal distribution of the averaged daily solar radiation and air temperature, the stochastic model satisfactorily simulated grain productivity, i.e., it was found that 10,604 kg ha(-1) is the most likely grain productivity, very similar to the productivity simulated by the deterministic model and for the real conditions based on a field experiment. © 2012 American Society of Agricultural and Biological Engineers.

What determines soil organic carbon stocks in the grazing lands of north-eastern Australia?

This study aimed to unravel the effects of climate, topography, soil, and grazing management on soil organic carbon (SOC) stocks in the grazing lands of north-eastern Australia. We sampled for SOC stocks at 98 sites from 18 grazing properties across Queensland, Australia. These samples covered four nominal grazing management classes (Continuous, Rotational, Cell, and Exclosure), eight broad soil types, and a strong tropical to subtropical climatic gradient. Temperature and vapour-pressure deficit explained >80% of the variability of SOC stocks at cumulative equivalent mineral masses nominally representing 0-0.1 and 0-0.3m depths. Once detrended of climatic effects, SOC stocks were strongly influenced by total standing dry matter, soil type, and the dominant grass species. At 0-0.3m depth only, there was a weak negative association between stocking rate and climate-detrended SOC stocks, and Cell grazing was associated with smaller SOC stocks than Continuous grazing and Exclosure. In future, collection of quantitative information on stocking intensity, frequency, and duration may help to improve understanding of the effect of grazing management on SOC stocks. Further exploration of the links between grazing management and above- and below-ground biomass, perhaps inferred through remote sensing and/or simulation modelling, may assist large-area mapping of SOC stocks in northern Australia. © CSIRO 2013.

Methods to determine resistance to anthelmintics when continuing larval development occurs

The in vivo faecal egg count reduction test (FECRT) is the most commonly used test to detect anthelmintic resistance (AR) in gastrointestinal nematodes (GIN) of ruminants in pasture based systems. However, there are several variations on the method, some more appropriate than others in specific circumstances. While in some cases labour and time can be saved by just collecting post-drench faecal worm egg counts (FEC) of treatment groups with controls, or pre- and post-drench FEC of a treatment group with no controls, there are circumstances when pre- and post-drench FEC of an untreated control group as well as from the treatment groups are necessary. Computer simulation techniques were used to determine the most appropriate of several methods for calculating AR when there is continuing larval development during the testing period, as often occurs when anthelmintic treatments against genera of GIN with high biotic potential or high re-infection rates, such as Haemonchus contortus of sheep and Cooperia punctata of cattle, are less than 100% efficacious. Three field FECRT experimental designs were investigated: (I) post-drench FEC of treatment and controls groups, (II) pre- and post-drench FEC of a treatment group only and (III) pre- and post-drench FEC of treatment and control groups. To investigate the performance of methods of indicating AR for each of these designs, simulated animal FEC were generated from negative binominal distributions with subsequent sampling from the binomial distributions to account for drench effect, with varying parameters for worm burden, larval development and drench resistance. Calculations of percent reductions and confidence limits were based on those of the Standing Committee for Agriculture (SCA) guidelines. For the two field methods with pre-drench FEC, confidence limits were also determined from cumulative inverse Beta distributions of FEC, for eggs per gram (epg) and the number of eggs counted at detection levels of 50 and 25. Two rules for determining AR: (1) %reduction (%R) < 95% and lower confidence limit <90%; and (2) upper confidence limit <95%, were also assessed. For each combination of worm burden, larval development and drench resistance parameters, 1000 simulations were run to determine the number of times the theoretical percent reduction fell within the estimated confidence limits and the number of times resistance would have been declared. When continuing larval development occurs during the testing period of the FECRT, the simulations showed AR should be calculated from pre- and post-drench worm egg counts of an untreated control group as well as from the treatment group. If the widely used resistance rule 1 is used to assess resistance, rule 2 should also be applied, especially when %R is in the range 90 to 95% and resistance is suspected.

Variation in water extraction with maize plant density and its impact on model application

The use of maize simulation models to determine the optimum plant population for rainfed environments allows the evaluation of plant populations over multiple years and locations at a lower cost than traditional field experimentation. However the APSIM maize model that has been used to conduct some of these 'virtual' experiments assumes that the maximum rate of soil water extraction by the crop root system is constant across plant populations. This untested assumption may cause grain yield to be overestimated in lower plant populations. A field experiment was conducted to determine whether maximum rates of water extraction vary with plant population, and the maximum rate of soil water extraction was estimated for three plant populations (2.4, 3.5 and 5.5 plants m(-2)) under water limited conditions. Maximum soil water extraction rates in the field experiment decreased linearly with plant population, and no difference was detected between plant populations for the crop lower limit of soil water extraction. Re-analysis of previous maize simulation experiments demonstrated that the use of inappropriately high extraction-rate parameters at low plant populations inflated predictions of grain yield, and could cause erroneous recommendations to be made for plant population. The results demonstrate the importance of validating crop simulation models across the range of intended treatments. (C) 2013 Elsevier E.V. All rights reserved.

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

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

Exploring the Potential Impacts of Climate Variability on Spring Wheat Yield with the APSIM Decision Support Tool

Assessing the impacts of climate variability on agricultural productivity at regional, national or global scale is essential for defining adaptation and mitigation strategies. We explore in this study the potential changes in spring wheat yields at Swift Current and Melfort, Canada, for different sowing windows under projected climate scenarios (i.e., the representative concentration pathways, RCP4.5 and RCP8.5). First, the APSIM model was calibrated and evaluated at the study sites using data from long term experimental field plots. Then, the impacts of change in sowing dates on final yield were assessed over the 2030-2099 period with a 1990-2009 baseline period of observed yield data, assuming that other crop management practices remained unchanged. Results showed that the performance of APSIM was quite satisfactory with an index of agreement of 0.80, R2 of 0.54, and mean absolute error (MAE) and root mean square error (RMSE) of 529 kg/ha and 1023 kg/ha, respectively (MAE = 476 kg/ha and RMSE = 684 kg/ha in calibration phase). Under the projected climate conditions, a general trend in yield loss was observed regardless of the sowing window, with a range from -24 to -94 depending on the site and the RCP, and noticeable losses during the 2060s and beyond (increasing CO2 effects being excluded). Smallest yield losses obtained through earlier possible sowing date (i.e., mid-April) under the projected future climate suggested that this option might be explored for mitigating possible adverse impacts of climate variability. Our findings could therefore serve as a basis for using APSIM as a decision support tool for adaptation/mitigation options under potential climate variability within Western Canada.

Using simulation techniques to investigate methods to determine resistance of helminths to anthelmintic treatment

The widespread and increasing resistance of internal parasites to anthelmintic control is a serious problem for the Australian sheep and wool industry. As part of control programmes, laboratories use the Faecal Egg Count Reduction Test (FECRT) to determine resistance to anthelmintics. It is important to have confidence in the measure of resistance, not only for the producer planning a drenching programme but also for companies investigating the efficacy of their products. The determination of resistance and corresponding confidence limits as given in anthelmintic efficacy guidelines of the Standing Committee on Agriculture (SCA) is based on a number of assumptions. This study evaluated the appropriateness of these assumptions for typical data and compared the effectiveness of the standard FECRT procedure with the effectiveness of alternative procedures. Several sets of historical experimental data from sheep and goats were analysed to determine that a negative binomial distribution was a more appropriate distribution to describe pre-treatment helminth egg counts in faeces than a normal distribution. Simulated egg counts for control animals were generated stochastically from negative binomial distributions and those for treated animals from negative binomial and binomial distributions. Three methods for determining resistance when percent reduction is based on arithmetic means were applied. The first was that advocated in the SCA guidelines, the second similar to the first but basing the variance estimates on negative binomial distributions, and the third using Wadley’s method with the distribution of the response variate assumed negative binomial and a logit link transformation. These were also compared with a fourth method recommended by the International Co-operation on Harmonisation of Technical Requirements for Registration of Veterinary Medicinal Products (VICH) programme, in which percent reduction is based on the geometric means. A wide selection of parameters was investigated and for each set 1000 simulations run. Percent reduction and confidence limits were then calculated for the methods, together with the number of times in each set of 1000 simulations the theoretical percent reduction fell within the estimated confidence limits and the number of times resistance would have been said to occur. These simulations provide the basis for setting conditions under which the methods could be recommended. The authors show that given the distribution of helminth egg counts found in Queensland flocks, the method based on arithmetic not geometric means should be used and suggest that resistance be redefined as occurring when the upper level of percent reduction is less than 95%. At least ten animals per group are required in most circumstances, though even 20 may be insufficient where effectiveness of the product is close to the cut off point for defining resistance.

Applying a simulation model to the management of an infestation of Miconia calvescens in the wet tropics of Australia

A simulation model that combines biological, search and economic components is applied to the eradication of a Miconia calvescens infestation at El Arish in tropical Queensland, Australia. Information on the year M. calvescens was introduced to the site, the number of plants controlled and the timing of control, is used to show that currently there could be M. calvescens plants remaining undetected at the site, including some mature plants. Modelling results indicate that the eradication programme has had a significant impact on the population of M. calvescens, as shown by simulated results for uncontrolled and controlled populations. The model was also used to investigate the effect of changing search effort on the cost of and time to eradication. Control costs were found to be negligible over all levels of search effort tested. Importantly, results suggest eradication may be achieved within several decades, if resources are increased slightly from their current levels and if there is a long-term commitment to funding the eradication programme.

Wet season resting - economic insights from scenario modelling

Pasture degradation, particularly that attributable to overgrazing, is a significant problem across the northern Australian rangelands. Although grazing studies have identified the scope for wet season resting strategies to be used to rehabilitate degraded pastures, the economic outcome of these strategies has not been extensively demonstrated. An exploratory study of the prospective economic value of wet season resting is presented using an economic simulation model of a 28000 ha beef enterprise located in the Charters Towers region of north-eastern Australia to explore seven hypothetical scenarios centred on the projected performance of a wet season resting strategy. A series of 20-year simulations for a range of pasture recovery profiles, stocking capacity, animal productivity responses, beef prices and agistment options are compared with a baseline scenario of taking no action. Estimates of the net present value of the 20-year difference in total enterprise gross margins between the various resting options and the 'do nothing' option identify that wet season resting can offer a positive economic return for the range of scenarios examined, although this is contingent on the assumptions that are made concerning the trajectories of change in carrying capacity and animal productivity. Some implications for management and policy making to support the practical implementation of wet season resting strategies are discussed.

Modelling the effect of plant water use traits on yield and stay-green expression in sorghum

Post-rainy sorghum (Sorghum bicolor (L.) Moench) production underpins the livelihood of millions in the semiarid tropics, where the crop is affected by drought. Drought scenarios have been classified and quantified using crop simulation. In this report, variation in traits that hypothetically contribute to drought adaptation (plant growth dynamics, canopy and root water conducting capacity, drought stress responses) were virtually introgressed into the most common post-rainy sorghum genotype, and the influence of these traits on plant growth, development, and grain and stover yield were simulated across different scenarios. Limited transpiration rates under high vapour pressure deficit had the highest positive effect on production, especially combined with enhanced water extraction capacity at the root level. Variability in leaf development (smaller canopy size, later plant vigour or increased leaf appearance rate) also increased grain yield under severe drought, although it caused a stover yield trade-off under milder stress. Although the leaf development response to soil drying varied, this trait had only a modest benefit on crop production across all stress scenarios. Closer dissection of the model outputs showed that under water limitation, grain yield was largely determined by the amount of water availability after anthesis, and this relationship became closer with stress severity. All traits investigated increased water availability after anthesis and caused a delay in leaf senescence and led to a ‘stay-green’ phenotype. In conclusion, we showed that breeding success remained highly probabilistic; maximum resilience and economic benefits depended on drought frequency. Maximum potential could be explored by specific combinations of traits.

An improved simulation model to predict pre-harvest aflatoxin risk in maize

Aflatoxin is a potent carcinogen produced by Aspergillus flavus, which frequently contaminates maize (Zea mays L.) in the field between 40° north and 40° south latitudes. A mechanistic model to predict risk of pre-harvest contamination could assist in management of this very harmful mycotoxin. In this study we describe an aflatoxin risk prediction model which is integrated with the Agricultural Production Systems Simulator (APSIM) modelling framework. The model computes a temperature function for A. flavus growth and aflatoxin production using a set of three cardinal temperatures determined in the laboratory using culture medium and intact grains. These cardinal temperatures were 11.5 °C as base, 32.5 °C as optimum and 42.5 °C as maximum. The model used a low (≤0.2) crop water supply to demand ratio—an index of drought during the grain filling stage to simulate maize crop's susceptibility to A. flavus growth and aflatoxin production. When this low threshold of the index was reached the model converted the temperature function into an aflatoxin risk index (ARI) to represent the risk of aflatoxin contamination. The model was applied to simulate ARI for two commercial maize hybrids, H513 and H614D, grown in five multi-location field trials in Kenya using site specific agronomy, weather and soil parameters. The observed mean aflatoxin contamination in these trials varied from <1 to 7143 ppb. ARI simulated by the model explained 99% of the variation (p ≤ 0.001) in a linear relationship with the mean observed aflatoxin contamination. The strong relationship between ARI and aflatoxin contamination suggests that the model could be applied to map risk prone areas and to monitor in-season risk for genotypes and soils parameterized for APSIM.