Lincoln Agritech (New Zealand)

Core Ideas A community effort is needed to move soil modeling forward. Establishing an international soil modeling consortium is key in this respect. There is a need to better integrate existing knowledge in soil models. Integration of data and models is a key challenge in soil modeling. The remarkable complexity of soil and its importance to a wide range of ecosystem services presents major challenges to the modeling of soil processes. Although major progress in soil models has occurred in the last decades, models of soil processes remain disjointed between disciplines or ecosystem services, with considerable uncertainty remaining in the quality of predictions and several challenges that remain yet to be addressed. First, there is a need to improve exchange of knowledge and experience among the different disciplines in soil science and to reach out to other Earth science communities. Second, the community needs to develop a new generation of soil models based on a systemic approach comprising relevant physical, chemical, and biological processes to address critical knowledge gaps in our understanding of soil processes and their interactions. Overcoming these challenges will facilitate exchanges between soil modeling and climate, plant, and social science modeling communities. It will allow us to contribute to preserve and improve our assessment of ecosystem services and advance our understanding of climate‐change feedback mechanisms, among others, thereby facilitating and strengthening communication among scientific disciplines and society. We review the role of modeling soil processes in quantifying key soil processes that shape ecosystem services, with a focus on provisioning and regulating services. We then identify key challenges in modeling soil processes, including the systematic incorporation of heterogeneity and uncertainty, the integration of data and models, and strategies for effective integration of knowledge on physical, chemical, and biological soil processes. We discuss how the soil modeling community could best interface with modern modeling activities in other disciplines, such as climate, ecology, and plant research, and how to weave novel observation and measurement techniques into soil models. We propose the establishment of an international soil modeling consortium to coherently advance soil modeling activities and foster communication with other Earth science disciplines. Such a consortium should promote soil modeling platforms and data repository for model development, calibration and intercomparison essential for addressing contemporary challenges.

Abstract The burgeoning global market for soil microbial inoculants for use in agriculture is being driven by pressure to increase sustainable crop production by managing pests and diseases without environmental impacts. Microbial inoculants, based predominantly on bacteria and fungi, are applied to soil as alternatives to conventional inorganic fertilizers (biofertilizers) or to carry out specific functions including biocontrol of pests and diseases (biopesticides), or for bioremediation and enhancement of soil characteristics. While some soil inoculants such as rhizobia have a long and successful history of use, others have performed inconsistently in the field and failed to live up to their promise suggested by laboratory testing. A more precise understanding of the ecology and modes of action of inoculant strains is key to optimizing their efficacy and guiding their targeted use to situations where they address key limitations to crop production. This will require greater collaboration between science disciplines, including microbiology, plant and soil science, molecular biology and agronomy. Inoculants must be produced and formulated to ensure their effective establishment in the soil and practicality of implementation alongside existing cropping practices. New approaches to strain selection and construction of beneficial microbial consortia should lead to more efficacious inoculant products. Extensive and rigorous field evaluation of inoculants under a range of soil and environmental conditions has rarely been undertaken and is urgently needed to validate emerging inoculant products and underpin successful implementation by growers, especially in a market that is largely unregulated at present.

Abstract Differential scanning calorimetry (DSC), optical microscopy, and X‐ray diffraction (XRD) were used to examine the thermal behavior, crystal structure, and crystal morphology of rice bran wax (RBX) in bulk and oil–wax mixtures, and to compare them with those of carnauba wax (CRX) and candellila wax (CLX). The RBX employed in the present study was separated from rice bran oil by winterization, filtration, refinement, bleaching, and deodorization. The RBX crystals melted in the bulk state at 77–79 °C with Δ H melting = 190.5 J/g, which is quite large compared with CLX (129 J/g) and CRX (137.6 J/g). XRD data of the RBX crystals revealed O ⊥ subcell packing and a long spacing value of 6.9 nm. Thin long needle‐shaped crystals were observed in the mixtures of RBX and liquid oils [olive oil and salad oil (canola:soy bean oil = 50:50)]; therefore, the dispersion of RBX crystals in these liquid oils was much finer than that of CRX and CLX crystals. Organogels formed when the mixture of every plant wax and liquid oil was melted at elevated temperature and cooled to ambient temperature. However, the mixture of RBX and olive oil at a concentration ratio of 1:99 wt.% formed an organogel at 20 °C, whereas the lowest concentration necessary for CRX to form an organogel in olive oil was 4 wt.% and that for CLX was 2 wt.%. Observation of the rate of gel formation using DSC and viscosity measurements indicated that the gel structure formed soon after RBX crystallized, whereas a time delay was observed between the organogel formation and wax crystallization of CRX and CLX. These results demonstrate RBX’s good organogel‐forming properties, mostly because of its fine dispersion of long needle like crystals in liquid oil phases.

Interests in the impact of the gastrointestinal microbiota on health and wellbeing have extended from humans to that of companion animals. While relatively fewer studies to date have examined canine and feline gut microbiomes, analysis of the metagenomic DNA from fecal communities using next-generation sequencing technologies have provided insights into the microbes that are present, their function, and potential to contribute to overall host nutrition and health. As carnivores, healthy dogs and cats possess fecal microbiomes that reflect the generally higher concentrations of protein and fat in their diets, relative to omnivores and herbivores. The phyla Firmicutes and Bacteroidetes are highly abundant, and Fusobacteria, Actinobacteria, and Proteobacteria also feature prominently. Proteobacteria is the most diverse bacterial phylum and commonly features in the fecal microbiota of healthy dogs and cats, although its reputation is often sullied as its members include a number of well-known opportunistic pathogens, such as Escherichia coli, Salmonella, and Campylobacter, which may impact the health of the host and its owner. Furthermore, in other host species, high abundances of Proteobacteria have been associated with dysbiosis in hosts with metabolic or inflammatory disorders. In this review, we seek to gain further insight into the prevalence and roles of the Proteobacteria within the gastrointestinal microbiomes of healthy dogs and cats. We draw upon the growing number of metagenomic DNA sequence-based studies which now allow us take a culture-independent approach to examine the functions that this more minor, yet important, group contribute to normal microbiome function.

Abstract Microbial degradation of DDT residues is one mechanism for loss of DDT from soil. In this review pathways for biodegradation of DDT, DDD, and DDE by bacteria and fungi are described. Biodegradation of DDT residues can proceed in soil, albeit at a slow rate. To enhance degradation in situ a number of strategies are proposed. They include the addition of DDT‐metabolising microbes to contaminated soils and/or the manipulation of environmental conditions to enhance the activity of these microbes. Ligninolytic fungi and chlorobiphenyl degrading bacteria are promising candidates for remediation. Flooding of soil and the addition of organic matter can enhance DDT degradation. As biodegradation may be inhibited by lack of access of the microbe to the contaminant, the soil may need to be pre‐treated with a surfactant. Unlike DDT, little is known about the biodegradation of DDE, and this knowledge is crucial as DDE can be the predominant residue in some soils.

Bayesian model selection or averaging objectively ranks a number of plausible, competing conceptual models based on Bayes' theorem. It implicitly performs an optimal trade-off between performance in fitting available data and minimum model complexity. The procedure requires determining Bayesian model evidence (BME), which is the likelihood of the observed data integrated over each model's parameter space. The computation of this integral is highly challenging because it is as high-dimensional as the number of model parameters. Three classes of techniques to compute BME are available, each with its own challenges and limitations: (1) Exact and fast analytical solutions are limited by strong assumptions. (2) Numerical evaluation quickly becomes unfeasible for expensive models. (3) Approximations known as information criteria (ICs) such as the AIC, BIC, or KIC (Akaike, Bayesian, or Kashyap information criterion, respectively) yield contradicting results with regard to model ranking. Our study features a theory-based intercomparison of these techniques. We further assess their accuracy in a simplistic synthetic example where for some scenarios an exact analytical solution exists. In more challenging scenarios, we use a brute-force Monte Carlo integration method as reference. We continue this analysis with a real-world application of hydrological model selection. This is a first-time benchmarking of the various methods for BME evaluation against true solutions. Results show that BME values from ICs are often heavily biased and that the choice of approximation method substantially influences the accuracy of model ranking. For reliable model selection, bias-free numerical methods should be preferred over ICs whenever computationally feasible.

To investigate how learning in immersive Virtual Reality was designed in contemporary educational studies, this systematic literature review identified nine design features and analysed 219 empirical studies on the designs of learning activities with immersive Virtual Reality. Overall, the technological features for physical presence were more readily implemented and investigated than pedagogical features for learning engagement. Further analysis with k-means clustering revealed five approaches with varying levels of interactivity and openness in learning tasks, from watching virtual worlds passively to responding to personalised prompts. Such differences in the design appeared to stem from different practical and educational priorities, such as accessibility, interactivity, and engagement. This review highlights the diversity in the learning task designs in immersive Virtual Reality and illustrates how researchers are navigating practical and educational concerns. We recommend future empirical studies recognise the different approaches and priorities when designing and evaluating learning with immersive Virtual Reality. We also recommend that future systematic reviews investigate immersive Virtual Reality-based learning not only by learning topics or learner demographics, but also by task designs and learning experiences.

ABSTRACT The influence of effective microorganisms (EM), a commercially available microbial inoculant containing yeasts, fungi, bacteria and actinomycetes, was evaluated in field trials of commercially produced, irrigated vegetable crops on "organic" farms in Canterbury, New Zealand during 1994–1995, and in a laboratory incubation. EM plus molasses were both applied, at 10 L ha□ 1 in 10,000 L ha□ 1 water, three times to the onions, twice to the peas and seven times to the sweetcorn. EM plus molasses increased the onion yield by 29% and the proportion of highest grade onions by 76%. EM plus molasses also increased pea yields by 31% and sweetcorn cob weights by 23%. A four week incubation at 30°C of loamy sand and 1% w/w pasture litter had treatments including a control, glucose, and EM plus glucose, and captured respired carbon (C) using NaOH traps. By the end of the incubation the glucose treatment had respired 38% more C than the control. The EM treatment respired an additional 8% more C than the glucose treatment. Using EM stimulated C mineralization in the laboratory incubation, but a corresponding increase in mineralization of organic nitrogen, phosphorus and sulphur was not measured. KEYWORDS: Effective microorganismsvegetable yieldpeassweet-cornonioncarbon mineralizationNew Zealand

-plant interaction.

Abstract Selecting a “best” model among several competing candidate models poses an often encountered problem in water resources modeling (and other disciplines which employ models). For a modeler, the best model fulfills a certain purpose best (e.g., flood prediction), which is typically assessed by comparing model simulations to data (e.g., stream flow). Model selection methods find the “best” trade‐off between good fit with data and model complexity. In this context, the interpretations of model complexity implied by different model selection methods are crucial, because they represent different underlying goals of modeling. Over the last decades, numerous model selection criteria have been proposed, but modelers who primarily want to apply a model selection criterion often face a lack of guidance for choosing the right criterion that matches their goal. We propose a classification scheme for model selection criteria that helps to find the right criterion for a specific goal, i.e., which employs the correct complexity interpretation. We identify four model selection classes which seek to achieve high predictive density, low predictive error, high model probability, or shortest compression of data. These goals can be achieved by following either nonconsistent or consistent model selection and by either incorporating a Bayesian parameter prior or not. We allocate commonly used criteria to these four classes, analyze how they represent model complexity and what this means for the model selection task. Finally, we provide guidance on choosing the right type of criteria for specific model selection tasks. (A quick guide through all key points is given at the end of the introduction.)

The complexity of karst groundwater flow modelling is reflected by the amount of simulation approaches. The goal of the Karst Modelling Challenge (KMC) is comparing different approaches on one single system using the same data set. Thirteen teams with different computational models for simulating discharge variations at karst springs have applied their respective models on one single data set coming from the Milandre Karst Hydrogeological System (MKHS). The approaches include neural networks, reservoir models, semi-distributed models and fully distributed groundwater models. Four and a half years of hourly or daily meteorological input and hourly discharge data were provided for model calibration. The validation comprised forecasting one year of discharge, without the observed discharge data. The model performance was evaluated using the volume conservation, Nash-Sutcliffe efficiency (NSE) and the Kling-Gupta efficiency (KGE) applied on the total discharge and individual flow components. As a result, the comparison of model performances is a challenging task due to the differences in the model architecture but also required time steps: some of the models require aggregated daily steps while others could be run using hourly data, which provided some interesting differences depending on how the data was transformed. The use of instantaneous data (e.g. value at noon) produces less bias that averaging hourly data over one day. The transformation of hourly into daily data produces a decrease of Nash and KGE of 0.05 to 0.08 (i.e. from 1 to ~0.93). The resulting simulations (forecasted values for year 2016) produced KGEs ranging between 0.83 and 0.37 (0.83 to −0.24 for NSE). Although the simulations matched the monitored flows reasonably well, most models struggled to simulate baseflow conditions accurately. In general, the models that performed the best for this exercise were the global ones (Gardenia and Varkarst), with a limited number of parameters, which can be calibrated using automatic calibration procedures. The neural network models also showed a fair potential, with one providing reasonable results despite the relatively short dataset available for warming-up (4.5 years). Semi-and fully distributed models also suggested that with some more effort they could perform well. The accuracy of model predictions does not seem to increase by using models with more than 9–12 calibration parameters. An evaluation of the relative errors between the forecasted and the observed values revealed that for most models, 50% of the forecasted values contained more than 50% of difference against the observed discharge rate, with 25% having a difference larger than 100%. A significant part of the poorly forecasted values corresponded to base-flow which was surprising given that as base-flow is generally much easier to predict than peak flow. Hence, this shows that modelling approaches and criteria for the calibration are too oriented towards peak-flow sections of the hydrographs, and that improvements could be gained by more focus on the base-flow.

Abstract With the longevity of phosphorus reserves uncertain, distributing phosphorus to meet food production needs is a global challenge. Here we match plant-available soil Olsen phosphorus concentrations to thresholds for optimal productivity of improved grassland and 28 of the world’s most widely grown and valuable crops. We find more land (73%) below optimal production thresholds than above. We calculate that an initial capital application of 56,954 kt could boost soil Olsen phosphorus to their threshold concentrations and that 28,067 kt yr −1 (17,500 kt yr −1 to cropland) could maintain these thresholds. Without additional reserves becoming available, it would take 454 years at the current rate of application (20,500 kt yr −1 ) to exhaust estimated reserves (2020 value), compared with 531 years at our estimated maintenance rate and 469 years if phosphorus deficits were alleviated. More judicious use of phosphorus fertilizers to account for soil Olsen phosphorus can help achieve optimal production without accelerating the depletion of phosphorus reserves.

Groundwater is a vital ecosystem of the global water cycle, hosting unique biodiversity and providing essential services to societies. Despite being the largest unfrozen freshwater resource, in a period of depletion by extraction and pollution, groundwater environments have been repeatedly overlooked in global biodiversity conservation agendas. Disregarding the importance of groundwater as an ecosystem ignores its critical role in preserving surface biomes. To foster timely global conservation of groundwater, we propose elevating the concept of keystone species into the realm of ecosystems, claiming groundwater as a keystone ecosystem that influences the integrity of many dependent ecosystems. Our global analysis shows that over half of land surface areas (52.6%) has a medium-to-high interaction with groundwater, reaching up to 74.9% when deserts and high mountains are excluded. We postulate that the intrinsic transboundary features of groundwater are critical for shifting perspectives towards more holistic approaches in aquatic ecology and beyond. Furthermore, we propose eight key themes to develop a science-policy integrated groundwater conservation agenda. Given ecosystems above and below the ground intersect at many levels, considering groundwater as an essential component of planetary health is pivotal to reduce biodiversity loss and buffer against climate change.

Land application has become a widely applied method for treating wastewater. However, it is not always clear which soil-plant systems should be used, or why. The objectives of our study were to determine if four contrasting soils, from which the pasture is regularly cut and removed, varied in their ability to assimilate nutrients from secondary-treated domestic effluent under high hydraulic loadings, in comparison with unirrigated, fertilized pasture. Grassed intact soil cores (500 mm in diameter by 700 mm in depth) were irrigated (50 mm wk(-1)) with secondary-treated domestic effluent for two years. Soils included a well-drained Allophanic Soil (Typic Hapludand), a poorly drained Gley Soil (Typic Endoaquept), a well-drained Pumice Soil formed from rhyolitic tephra (Typic Udivitrand), and a well-drained Recent Soil formed in a sand dune (Typic Udipsamment). Effluent-irrigated soils received between 746 and 815 kg N ha(-1) and 283 and 331 kg P ha(-1) over two years of irrigation, and unirrigated treatments received 200 kg N ha(-1) and 100 kg P ha(-1) of dissolved inorganic fertilizer over the same period. Applying effluent significantly increased plant uptake of N and P from all soil types. For the effluent-irrigated soils plant N uptake ranged from 186 to 437 kg N ha(-1) yr(-1), while plant P uptake ranged from 40 to 88 kg P ha(-1) yr(-1) for the effluent-irrigated soils. Applying effluent significantly increased N leaching losses from Gley and Recent Soils, and after two years ranged from 17 to 184 kg N ha(-1) depending on soil type. Effluent irrigation only increased P leaching from the Gley Soil. All P leaching losses were less than 49 kg P ha(-1) after two years. The N and P leached from effluent treatments were mainly in organic form (69-87% organic N and 35-65% unreactive P). Greater N and P leaching losses from the irrigated Gley Soil were attributed to preferential flow that reduced contact between the effluent and the soil matrix. Increased N leaching from the Recent Soil was the result of increased leaching of native soil organic N due to the higher hydraulic loading from the effluent irrigation.

Calibration, the estimation of model parameters based on fitting the model to experimental data, is among the first steps in many applications of process-based models and has an important impact on simulated values. We propose a novel method of developing guidelines for calibration of process-based models, based on development of recommendations for calibration of the phenology component of crop models. The approach was based on a multi-model study, where all teams were provided with the same data and asked to return simulations for the same conditions. All teams were asked to document in detail their calibration approach, including choices with respect to criteria for best parameters, choice of parameters to estimate and software. Based on an analysis of the advantages and disadvantages of the various choices, we propose calibration recommendations that cover a comprehensive list of decisions and that are based on actual practices.

The pasture growth module AgPasture was integrated into the APSIM (Agricultural Production System Simulator) simulation model, allowing pasture‐based systems to be modelled in combination with other land uses at farm scale or within land use change studies. The model's predictions of pasture growth were evaluated against 32 pasture growth datasets from a diverse range of soil types and climatic zones across New Zealand. The pasture herbage accumulation simulated by the model closely matched actual measurements over varying intervals. Both predicted and measured pasture growth rate demonstrated the same seasonal pattern, including mean growth rate and inter‐annual variation across measurement years. Predicted and measured annual average net herbage accumulation (NHA) on a dryland pasture was similar over 37 observation years (mean, 6.83 and 7.27 t DM/ha respectively; coefficient of variation, 29% and 27% respectively) and highly correlated ( R 2 = 0.838, P < 0.0001; relative root mean squared deviation (RMSD) = 16%). The model's prediction of annual average NHA of all simulated pastures, spanning a wide range of pasture environments, also matched the measurement data well ( R 2 = 0.777, P < 0.0001; relative RMSD = 21%). However, discrepancies between simulated and observed values occurred in some seasons and at some sites. Analysis of these discrepancies identified areas where the model could be improved by incorporating more accurate descriptions of the effects of plant development and grazing, soil temperature and the interactive effects of high temperature and soil moisture dynamics.

Rice bran wax (RBX) obtained during rice bran oil purification can form organogels in edible oils. The kinetics of crystallization and the viscous properties of RBX organogels were studied using differential scanning calorimetry (DSC), viscosity changes with varying temperature, hardness measurements by penetrometry, and synchrotron radiation X-ray diffraction (SR-XRD). The organogels were prepared by RBX in concentrations of 1%, 3%, 6%, and 10% on a weight basis in salad oil, olive oil, and camellia oil. The liquid oil type had no significant effect on the melting and crystallization temperatures of the RBX. However, the viscosity and the texture of the organogels differed with liquid oil type, temperature, and RBX concentration. Changes in the viscosity of the RBX organogels were monitored during cooling from 80°C to 20°C. Drastic viscosity changes occurred in accordance with the onset of crystallization in DSC thermographs obtained at a rate of 5°C/min. RBX in the olive oil and camellia oil mixtures had higher viscosity than RBX in the salad oil mixture, which correlates with the hardness obtained in texture measurements at 20°C. SR-XRD was used to clarify the crystal structures of the building blocks of the RBX organogels in salad oil. It was found that the RBX formed crystals with a long spacing of 7.3 ± 1 nm and short spacings of 0.41 ± 1 nm and 0.37 ± 1 nm. The intensity of the long-spacing pattern was remarkably weaker than that of the short-spacing patterns, which demonstrated strong anisotropy in the crystal growth of RBX crystal particles.

Abstract The source, form, and fate of DDT residues in the environment are reviewed. Discussion is primarily from a New Zealand perspective, where a major use of DDT was the control of soil‐dwelling pasture pests. Reasons for the persistence of DDT residues, the association between residues and soil components, and possible degradative and non‐degradative losses from soils are discussed.

O) exchange using non-steady-state chambers is converting collected gas concentration versus time data to flux values using a flux calculation (FC) scheme. It is well documented that different FC schemes can produce different flux estimates for a given set of data. Available schemes differ in their theoretical basis, computational requirements, and performance in terms of both accuracy and precision. Nonlinear schemes tend to increase accuracy compared with linear regression but can also decrease precision. The chamber bias correction method can be used if soil physical data are available, but this introduces additional sources of error. Here, the essential theoretical and practical aspects of the most commonly used FC schemes are described as a basis for their selection and use. A gold standard approach for application and selection of FC schemes is presented, as well as alternative approaches based on availability of soil physical property data and intensity of sample collection during each chamber deployment. Additional criteria for scheme selection are provided in the form of an error analysis tool that quantifies performance with respect to both accuracy and precision based on chamber dimensions and sampling duration, soil properties, and analytical measurement precision. Example error analyses are presented for hypothetical conditions illustrating how such analysis can be used to guide FC scheme selection, estimate bias, and inform design of chambers and sampling regimes.

KEY MESSAGE: Genomic prediction models for multi-year dry matter yield, via genotyping-by-sequencing in a composite training set, demonstrate potential for genetic gain improvement through within-half sibling family selection. Perennial ryegrass (Lolium perenne L.) is a key source of nutrition for ruminant livestock in temperate environments worldwide. Higher seasonal and annual yield of herbage dry matter (DMY) is a principal breeding objective but the historical realised rate of genetic gain for DMY is modest. Genomic selection was investigated as a tool to enhance the rate of genetic gain. Genotyping-by-sequencing (GBS) was undertaken in a multi-population (MP) training set of five populations, phenotyped as half-sibling (HS) families in five environments over 2 years for mean herbage accumulation (HA), a measure of DMY potential. GBS using the ApeKI enzyme yielded 1.02 million single-nucleotide polymorphism (SNP) markers from a training set of n = 517. MP-based genomic prediction models for HA were effective in all five populations, cross-validation-predictive ability (PA) ranging from 0.07 to 0.43, by trait and target population, and 0.40-0.52 for days-to-heading. Best linear unbiased predictor (BLUP)-based prediction methods, including GBLUP with either a standard or a recently developed (KGD) relatedness estimation, were marginally superior or equal to ridge regression and random forest computational approaches. PA was principally an outcome of SNP modelling genetic relationships between training and validation sets, which may limit application for long-term genomic selection, due to PA decay. However, simulation using data from the training experiment indicated a twofold increase in genetic gain for HA, when applying a prediction model with moderate PA in a single selection cycle, by combining among-HS family selection, based on phenotype, with within-HS family selection using genomic prediction.