Modeling chickpea growth and development: Nitrogen accumulation and use

Quantitative information regarding nitrogen (N) accumulation and its distribution to leaves, stems and grains under varying environmental and growth conditions are limited for chickpea (Cicer arietinum L.). The information is required for the development of crop growth models and also for assessment of the contribution of chickpea to N balances in cropping systems. Accordingly, these processes were quantified in chickpea under different environmental and growth conditions (still without water or N deficit) using four field experiments and 1325 N measurements. N concentration ([N]) in green leaves was 50 mg g-1 up to beginning of seed growth, and then it declined linearly to 30 mg g-1 at the end of seed growth phase. [N] in senesced leaves was 12 mg g-1. Stem [N] decreased from 30 mg g-1 early in the season to 8 mg g-1 in senesced stems at maturity. Pod [N] was constant (35 mg g-1), but grain [N] decreased from 60 mg g-1 early in seed growth to 43 mg g-1 at maturity. Total N accumulation ranged between 9 and 30 g m-2. N accumulation was closely linked to biomass accumulation until maturity. N accumulation efficiency (N accumulation relative to biomass accumulation) was 0.033 g g-1 where total biomass was -2 and during early growth period, but it decreased to 0.0176 g g-1 during the later growth period when total biomass was >218 g m-2. During vegetative growth (up to first-pod), 58% of N was partitioned to leaves and 42% to stems. Depending on growth conditions, 37-72% of leaf N and 12-56% of stem N was remobilized to the grains. The parameter estimates and functions obtained in this study can be used in chickpea simulation models to simulate N accumulation and distribution.

Modeling population growth and site specific control of the invasive Lantana camara L. (Verbenaceae) under differing fire regimes

It is at the population level that an invasion either fails or succeeds. Lantana camara L. (Verbenaceae) is a weed of great significance in Queensland Australia and globally but its whole life-history ecology is poorly known. Here we used 3 years of field data across four land use types (farm, hoop pine plantation and two open eucalyptus forests, including one with a triennial fire regime) to parameterise the weed’s vital rates and develop size-structured matrix models. Lantana camara in its re-colonization phase, as observed in the recently cleared hoop pine plantation, was projected to increase more rapidly (annual growth rate, λ = 3.80) than at the other three sites (λ 1.88–2.71). Elasticity analyses indicated that growth contributed more (64.6 %) to λ than fecundity (18.5 %) or survival (15.5 %), while across size groups, the contribution was of the order: juvenile (19–27 %) ≥ seed (17–28 %) ≥ seedling (16–25 %) > small adult (4–26 %) ≥ medium adult (7–20 %) > large adult (0–20 %). From a control perspective it is difficult to determine a single weak point in the life cycle of lantana that might be exploited to reduce growth below a sustaining rate. The triennial fire regime applied did not alter the population elasticity structure nor resulted in local control of the weed. However, simulations showed that, except for the farm population, periodic burning could work within 4–10 years for control of the weed, but fire frequency should increase to at least once every 2 years. For the farm, site-specific control may be achieved by 15 years if the biennial fire frequency is tempered with increased burning intensity.

Scale- and Context-Dependent Selection of Recreational Harvest Estimation Methods: The Australasian Experience

Fisheries managers are becoming increasingly aware of the need to quantify all forms of harvest, including that by recreational fishers. This need has been driven by both a growing recognition of the potential impact that noncommercial fishers can have on exploited resources and the requirement to allocate catch limits between different sectors of the wider fishing community in many jurisdictions. Marine recreational fishers are rarely required to report any of their activity, and some form of survey technique is usually required to estimate levels of recreational catch and effort. In this review, we describe and discuss studies that have attempted to estimate the nature and extent of recreational harvests of marine fishes in New Zealand and Australia over the past 20 years. We compare studies by method to show how circumstances dictate their application and to highlight recent developments that other researchers may find of use. Although there has been some convergence of approach, we suggest that context is an important consideration, and many of the techniques discussed here have been adapted to suit local conditions and to address recognized sources of bias. Much of this experience, along with novel improvements to existing approaches, have been reported only in "gray" literature because of an emphasis on providing estimates for immediate management purposes. This paper brings much of that work together for the first time, and we discuss how others might benefit from our experience.

High-Throughput Prediction of Acacia and Eucalypt Lignin Syringyl/Guaiacyl Content Using FT-Raman Spectroscopy and Partial Least Squares Modeling

High-throughput techniques are necessary to efficiently screen potential lignocellulosic feedstocks for the production of renewable fuels, chemicals, and bio-based materials, thereby reducing experimental time and expense while supplanting tedious, destructive methods. The ratio of lignin syringyl (S) to guaiacyl (G) monomers has been routinely quantified as a way to probe biomass recalcitrance. Mid-infrared and Raman spectroscopy have been demonstrated to produce robust partial least squares models for the prediction of lignin S/G ratios in a diverse group of Acacia and eucalypt trees. The most accurate Raman model has now been used to predict the S/G ratio from 269 unknown Acacia and eucalypt feedstocks. This study demonstrates the application of a partial least squares model composed of Raman spectral data and lignin S/G ratios measured using pyrolysis/molecular beam mass spectrometry (pyMBMS) for the prediction of S/G ratios in an unknown data set. The predicted S/G ratios calculated by the model were averaged according to plant species, and the means were not found to differ from the pyMBMS ratios when evaluating the mean values of each method within the 95 % confidence interval. Pairwise comparisons within each data set were employed to assess statistical differences between each biomass species. While some pairwise appraisals failed to differentiate between species, Acacias, in both data sets, clearly display significant differences in their S/G composition which distinguish them from eucalypts. This research shows the power of using Raman spectroscopy to supplant tedious, destructive methods for the evaluation of the lignin S/G ratio of diverse plant biomass materials.

Projecting future expansion of invasive species: Comparing and improving methodologies for species distribution modeling

Modeling the distributions of species, especially of invasive species in non-native ranges, involves multiple challenges. Here, we developed some novel approaches to species distribution modeling aimed at reducing the influences of such challenges and improving the realism of projections. We estimated species-environment relationships with four modeling methods run with multiple scenarios of (1) sources of occurrences and geographically isolated background ranges for absences, (2) approaches to drawing background (absence) points, and (3) alternate sets of predictor variables. We further tested various quantitative metrics of model evaluation against biological insight. Model projections were very sensitive to the choice of training dataset. Model accuracy was much improved by using a global dataset for model training, rather than restricting data input to the species’ native range. AUC score was a poor metric for model evaluation and, if used alone, was not a useful criterion for assessing model performance. Projections away from the sampled space (i.e. into areas of potential future invasion) were very different depending on the modeling methods used, raising questions about the reliability of ensemble projections. Generalized linear models gave very unrealistic projections far away from the training region. Models that efficiently fit the dominant pattern, but exclude highly local patterns in the dataset and capture interactions as they appear in data (e.g. boosted regression trees), improved generalization of the models. Biological knowledge of the species and its distribution was important in refining choices about the best set of projections. A post-hoc test conducted on a new Partenium dataset from Nepal validated excellent predictive performance of our “best” model. We showed that vast stretches of currently uninvaded geographic areas on multiple continents harbor highly suitable habitats for Parthenium hysterophorus L. (Asteraceae; parthenium). However, discrepancies between model predictions and parthenium invasion in Australia indicate successful management for this globally significant weed. This article is protected by copyright. All rights reserved.

Projecting future expansion of invasive species: Comparing and improving methodologies for species distribution modeling

Modeling the distributions of species, especially of invasive species in non-native ranges, involves multiple challenges. Here, we developed some novel approaches to species distribution modeling aimed at reducing the influences of such challenges and improving the realism of projections. We estimated species-environment relationships with four modeling methods run with multiple scenarios of (1) sources of occurrences and geographically isolated background ranges for absences, (2) approaches to drawing background (absence) points, and (3) alternate sets of predictor variables. We further tested various quantitative metrics of model evaluation against biological insight. Model projections were very sensitive to the choice of training dataset. Model accuracy was much improved by using a global dataset for model training, rather than restricting data input to the species’ native range. AUC score was a poor metric for model evaluation and, if used alone, was not a useful criterion for assessing model performance. Projections away from the sampled space (i.e. into areas of potential future invasion) were very different depending on the modeling methods used, raising questions about the reliability of ensemble projections. Generalized linear models gave very unrealistic projections far away from the training region. Models that efficiently fit the dominant pattern, but exclude highly local patterns in the dataset and capture interactions as they appear in data (e.g. boosted regression trees), improved generalization of the models. Biological knowledge of the species and its distribution was important in refining choices about the best set of projections. A post-hoc test conducted on a new Partenium dataset from Nepal validated excellent predictive performance of our “best” model. We showed that vast stretches of currently uninvaded geographic areas on multiple continents harbor highly suitable habitats for Parthenium hysterophorus L. (Asteraceae; parthenium). However, discrepancies between model predictions and parthenium invasion in Australia indicate successful management for this globally significant weed. This article is protected by copyright. All rights reserved.

High-throughput Phenotyping of Wheat Seminal Root Traits in a Breeding Context

Water availability is a major limiting factor for wheat (Triticum aestivum L.) in rain-fed agricultural systems worldwide. Root architecture has important functional implications for the timing and extent of soil water extraction, yet selection for root traits in wheat breeding programs has been largely limited due to the lack of suitable phenotyping methods. The aim of this research was to develop a low-cost high-throughput phenotyping method to facilitate selection for desirable root traits. We developed a method to assess ‘seminal root angle’ and ‘seminal root number’ in seedlings – two proxy traits associated to root architecture of mature wheat plants (1). The method involves measuring the angle between the first pair of seminal roots and the number of roots of wheat seedlings grown in transparent pots (Figure 1). Images captured at 5 to 10 days after sowing are analyzed to calculate seminal root angle and number. Performing this technique under “speed breeding” conditions (plants grown at a density of 600 plants / m2, under controlled temperature and constant light) allows the selection based on the desired root traits of up to 5 consecutive generations within 12 months. Alternatively, when focusing only on germplasm screening, up to 52 successive phenotypic assays can be conducted within 12 months. This approach has been shown to be highly reproducible, it requires little resource (time, space, and labour) and can be used to rapidly enrich breeding populations with desirable alleles for narrow root angle and a high number of seminal roots to indirectly target the selection of deeper root system with higher branching at depth. Such root characteristics are highly desirable in wheat to cope with the climate model projections, especially in summer rainfall dominant regions including some Australian, Indian, South American and African cropping regions, where winter crops mainly rely on deep stored water.